Low-power active damper adjustment method and system based on environmental changes

Abstract

The application discloses a low-power active air dam adjusting method and system based on environmental changes, and relates to the technical field of air dam adjustment. The method comprises the following steps: collecting multi-modal driving environment data, classifying the multi-modal driving environment data according to working conditions, and determining the current driving environment working condition; combining navigation road condition data to analyze the air dam opening degree demand of the current driving environment working condition, and obtaining an air dam opening degree pre-adjustment target value; obtaining the current air dam actual opening degree, and calculating the opening degree difference between the current air dam actual opening degree and the air dam opening degree pre-adjustment target value; and constructing a hierarchical low-power opening degree adjustment strategy library, and performing adaptive strategy matching and hierarchical low-power adjustment on the opening degree difference. The technical problems that the active air dam adjustment process in the prior art is difficult to adapt to the dynamically changing driving environment, resulting in insufficient air dam opening degree adjustment accuracy and high energy consumption in the adjustment process are solved, and the technical effects of realizing active air dam opening degree self-adaptive adjustment based on environmental changes and reducing the energy consumption in the adjustment process are achieved.

Description

Technical Field

[0001] This invention relates to the field of air dam regulation technology, specifically to a low-power active air dam regulation method and system based on environmental changes. Background Technology

[0002] Active air dams are used to regulate airflow under vehicles, reducing air resistance during driving by changing the dam's extension / retraction or opening degree, thereby improving vehicle stability and energy efficiency. Existing active air dam control methods typically adjust the opening degree based on preset driving speeds, fixed road condition parameters, or simple threshold conditions. However, in actual driving, the vehicle's environment is affected by various factors such as road slope, wind speed changes, traffic conditions, and driving conditions, leading to continuously changing aerodynamic requirements. Fixed control methods struggle to match these changing needs in a timely manner. Furthermore, existing active air dams often employ continuous response or single adjustment amplitude control during adjustment. Even with minor environmental changes, they may still perform large adjustments, causing frequent actuator movements, increasing system energy consumption, and affecting equipment lifespan. Therefore, existing active air dam adjustment methods suffer from insufficient environmental adaptability, low opening degree control accuracy, and inadequate low-power operation capabilities, failing to meet the demands of vehicles for aerodynamic performance optimization and energy-saving operation in complex dynamic environments. Summary of the Invention

[0003] This application provides a low-power active air dam adjustment method and system based on environmental changes, which solves the technical problems in the prior art where the active air dam adjustment process is difficult to adapt to the dynamically changing driving environment, resulting in insufficient accuracy of air dam opening adjustment and high energy consumption in the adjustment process.

[0004] The first aspect of this application provides a low-power active air dam regulation method based on environmental changes, the method comprising: A sensor network is deployed around the target active air dam to collect multimodal driving environment data. This data is then categorized by operating condition to determine the current driving environment condition. Combined with navigation traffic data, air dam opening demand analysis is performed to obtain a pre-adjustment target value. The actual air dam opening is then obtained, and the difference between the actual opening and the pre-adjustment target value is calculated. A tiered low-power opening adjustment strategy library is constructed, and adaptive strategy matching and tiered low-power adjustment are applied to the opening difference based on this library.

[0005] A second aspect of this application provides a low-power active air dam regulation system based on environmental changes, the system comprising: Data Acquisition Module: Deploys a sensor network around the target active air dam, collects multimodal driving environment data through the sensor network, classifies the multimodal driving environment data according to operating conditions, and determines the current driving environment operating conditions; Demand Analysis Module: Combines navigation traffic data to perform air dam opening demand analysis on the current driving environment operating conditions, and obtains the air dam opening pre-adjustment target value; Calculation Module: Obtains the current actual air dam opening, and calculates the opening difference between the current actual air dam opening and the air dam opening pre-adjustment target value; Matching and Adjustment Module: Constructs a tiered low-power opening adjustment strategy library, and performs adaptive strategy matching and tiered low-power adjustment on the opening difference based on the tiered low-power opening adjustment strategy library.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, a sensor network is deployed around the target active airdam to collect multimodal driving environment data. This data is then categorized by operating condition to determine the current driving environment. Next, navigation traffic data is used to analyze the airdam opening requirement under the current driving environment, yielding a pre-adjustment target value. Then, the actual airdam opening is obtained, and the difference between the actual opening and the pre-adjustment target value is calculated. Finally, a tiered low-power opening adjustment strategy library is constructed. Based on this library, adaptive strategy matching and tiered low-power adjustment are performed on the opening difference. This solves the technical problems in existing technologies where the active airdam adjustment process struggles to adapt to dynamically changing driving environments, leading to insufficient accuracy and high energy consumption. It achieves the technical effect of adaptive adjustment of the active airdam opening based on environmental changes while reducing energy consumption during the adjustment process. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic flowchart of a low-power active air dam regulation method based on environmental changes provided in an embodiment of this application; Figure 2 A schematic diagram of a low-power active air dam regulation system based on environmental changes, provided in an embodiment of this application.

[0009] Figure labeling: Data acquisition module 11, demand analysis module 12, calculation module 13, matching and adjustment module 14. Detailed Implementation

[0010] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0011] Example 1, as Figure 1 As shown, this application provides a low-power active air dam regulation method based on environmental changes, wherein the method includes: A sensor network is deployed around the target active air dam to collect multimodal driving environment data. The multimodal driving environment data is then classified according to operating conditions to determine the current driving environment operating conditions.

[0012] A sensor network is deployed around the target active air dam. This sensor network includes vehicle status sensors for collecting vehicle operating status, environmental sensors for collecting the surrounding environment status, and aerodynamic sensing sensors for collecting airflow changes. The vehicle status sensors collect data on the vehicle's current speed, acceleration, drive load, and power output. The environmental sensors collect data on the ambient temperature, ambient pressure, wind speed, and wind direction corresponding to the vehicle's driving area. The aerodynamic sensing sensors collect data on airflow speed, airflow pressure, and airflow disturbance changes around the active air dam. The data collected by each sensor are synchronized using a unified time base to form a multi-source synchronized driving environment dataset. This multi-source synchronized driving environment dataset undergoes data cleaning to remove abnormal sampling values, missing data, and data that significantly deviate from the expected values. Data with a constantly changing range is collected and normalized for different types of data to obtain multimodal driving environment data that can be used for analysis. Driving-related operating condition features are further extracted from the multimodal driving environment data. These features include vehicle speed change features, load change features, airflow disturbance features, and environmental change features. Based on preset operating condition classification rules, the driving-related operating condition features are matched with the feature boundary conditions corresponding to each preset driving environment operating condition. When the vehicle speed change features, load change features, airflow disturbance features, and environmental change features meet the corresponding operating condition boundary conditions, the current driving state is classified into the corresponding driving environment operating condition category, thus determining the current driving environment operating condition. The current driving environment operating condition includes at least one of the following: high-speed stable driving condition, low-speed congested driving condition, slope road condition, and strong disturbance environment condition.

[0013] Furthermore, the multimodal driving environment data is classified according to operating conditions to determine the current driving environment operating conditions, including: The multimodal driving environment data is time-axis aligned and cleaned to obtain usable multimodal driving environment data. A set of driving-related operating condition features is extracted from the usable multimodal driving environment data. The types of the set of driving-related operating condition features include driving features, thermal management features, and environmental features. Preset driving operating condition boundary rules are used to classify the driving-related operating condition features to determine the current driving environment operating condition.

[0014] Preferably, the sampling timestamps corresponding to the data collected by each sensor in the sensor network are obtained. Using the reference sampling period of the vehicle control system as a unified time reference, vehicle speed data, acceleration data, drive load data, airflow pressure data, ambient temperature data, ambient pressure data, and wind speed and direction data are matched according to their timestamps. When there is a deviation in the sampling times of data from different sensors, interpolation is performed based on the data change trends of adjacent sampling time nodes to map multimodal driving environment data from different sources to the same time series, obtaining time-synchronized driving environment data. Further, anomaly detection is performed on the time-synchronized driving environment data. The detection process identifies abnormal sampled values ​​that exceed the normal variation range for each data type and the data variation amplitude within a preset sliding time window. Abnormal data is corrected using a weighted average of adjacent valid sampled data. For missing sampled data, compensation is performed based on the variation trend of valid sampled data before and after the missing time point, resulting in continuous and complete usable multimodal driving environment data. Feature extraction is performed based on this usable multimodal driving environment data. The vehicle speed change rate is calculated based on vehicle speed data within the continuous time window, the vehicle acceleration state change is calculated based on acceleration data, and the vehicle power demand is calculated based on drive load data. The changes are calculated based on vehicle temperature data to determine the temperature trend, airflow pressure and velocity data to determine the aerodynamic heat transfer characteristics, and ambient temperature, pressure, wind speed, and direction data to determine the environmental disturbance characteristics. These factors are then combined according to their respective data types to form a set of driving-related operating condition characteristics that includes driving characteristics, thermal management characteristics, and environmental characteristics. Furthermore, driving condition boundary rules are established based on historical vehicle operating data to define the corresponding speed ranges and load factors under different vehicle operating conditions. The load variation range, temperature variation range, and environmental disturbance range are associated and configured to form feature constraints corresponding to different types of driving environment conditions. Each feature parameter in the driving environment condition feature set is matched with the driving environment condition boundary rules. When each feature parameter satisfies the feature constraint condition corresponding to a certain driving environment condition, the driving environment condition is determined as the current driving environment condition. When the driving environment condition feature set satisfies the feature constraints corresponding to multiple driving environment conditions, the feature matching degree corresponding to each condition is calculated, and the driving environment condition with the highest matching degree is selected as the current driving environment condition output.

[0015] Establishing driving condition boundary rules based on historical vehicle operation data includes: acquiring historical operation data of vehicles under different road environments, driving states, and climatic conditions, wherein the historical operation data includes historical vehicle speed data, historical acceleration data, historical drive load data, historical vehicle temperature data, historical airflow state data, and historical environmental parameter data; segmenting the historical operation data according to a preset time window, and performing feature calculations on the corresponding vehicle speed change rate, acceleration change, drive load change, temperature change trend, airflow disturbance, and environmental disturbance within each time window to form a historical operating condition feature sample set; and based on the historical operating condition feature sample set, adjusting the historical operating condition features according to the feature change patterns under different vehicle operating states. The samples are classified and labeled, and historical operating condition feature samples with the same aerodynamic adjustment requirements are grouped into the same operating condition category. For each operating condition category, the value range of each operating condition feature parameter under that category is statistically analyzed, and occasional abnormal data is removed according to the preset confidence interval to determine the feature boundary range corresponding to each operating condition category. Among them, the vehicle speed change rate and the amount of drive load change are used as the driving state boundary parameters, the temperature change trend and the aerodynamic heat transfer change characteristics are used as the thermal management state boundary parameters, and the wind speed, the amount of ambient pressure change, and the amount of airflow disturbance are used as the environmental state boundary parameters. The ranges of each boundary parameter are combined and associated to generate driving operating condition boundary rules corresponding to different types of driving environment operating conditions, which are used for subsequent operating condition matching based on the real-time collected driving associated operating condition feature set.

[0016] By combining navigation traffic data to analyze the air dam opening requirements of the current driving environment, the pre-adjustment target value of the air dam opening is obtained.

[0017] Furthermore, by combining navigation traffic data with the current driving environment conditions to analyze the air dam opening demand, the pre-adjustment target value for the air dam opening is obtained, including: Based on historical opening experience data of active air dams, an air dam operating condition benchmark opening library is constructed; the air dam operating condition benchmark opening library is used to match the opening requirements of the current driving environment to obtain an initial air dam opening benchmark value; the initial air dam opening benchmark value is dynamically adjusted in conjunction with navigation road condition data to obtain the air dam opening pre-adjustment target value.

[0018] Preferably, active air dam opening operation data are collected during historical vehicle driving processes. Simultaneously, vehicle speed, vehicle load status, ambient temperature, ambient pressure, wind speed, road gradient, and actual air dam opening data for each historical moment are acquired. The historical operating data is categorized into operating conditions based on driving characteristics, thermal management characteristics, and environmental characteristics. For each historical driving environment condition, the corresponding actual air dam opening variation pattern is statistically analyzed. The average air dam opening, opening variation range, and corresponding environmental parameter distribution under each operating condition are calculated. A correlation mapping relationship between driving environment operating condition characteristics and air dam opening parameters is established, generating an air dam operating condition benchmark opening library. The current driving environment operating condition's corresponding driving-related operating condition feature set is obtained. After normalizing the vehicle speed, load status, thermal management status, and environmental status parameters in the driving-related operating condition feature set, similarity matching is performed with the historical operating condition features in the air dam operating condition benchmark opening library to calculate the similarity between the current driving environment operating condition and the historical operating condition features. The characteristic distances between various historical operating conditions are calculated, and the historical operating condition with the smallest characteristic distance is selected as the target matching operating condition. The air dam reference opening value corresponding to the target matching operating condition is called to obtain the initial air dam opening reference value. Further, vehicle navigation traffic data is acquired, and the changes in road slope, road type, expected speed change trend, and road traffic status within a preset distance range ahead of the vehicle in the navigation traffic data are analyzed. The road condition influence features affecting the active air dam opening requirement are extracted, and the slope correction coefficient, speed trend correction coefficient, and road condition adjustment weight are calculated based on the road slope change, expected speed change, and road traffic status, respectively. The slope correction coefficient, speed trend correction coefficient, and road condition adjustment weight are then fused to obtain the navigation traffic correction amount. Based on the navigation traffic correction amount, the initial air dam opening reference value is dynamically corrected to obtain the air dam opening pre-adjustment target value, wherein the air dam opening pre-adjustment target value is calculated in the following way: ,in, This indicates the pre-adjustment target value for the air dam opening. This represents the initial air dam opening reference value. This represents the speed correction amount obtained based on the expected trend of vehicle speed changes. This represents the slope correction amount obtained based on changes in road slope. This represents the vehicle speed trend correction coefficient. This represents the slope correction factor; through the above calculations, the active air dam can determine the target opening degree in advance based on the current driving environment and the road conditions the vehicle is about to travel on.

[0019] Furthermore, by dynamically adjusting the initial air dam opening benchmark value in conjunction with navigation traffic data, a pre-adjustment target value for the air dam opening is obtained, including: Extract the set of road condition influence features related to the opening degree corresponding to the navigation road condition data; perform an influence degree analysis on the set of road condition influence features related to the opening degree to generate a vehicle speed trend correction coefficient and a slope load correction coefficient; dynamically adjust the initial air dam opening benchmark value based on the vehicle speed trend correction coefficient and the slope load correction coefficient to obtain the air dam opening pre-adjustment target value.

[0020] Preferably, road condition information within a preset forward-looking distance range output by the vehicle navigation system is acquired. This road condition information includes road slope, road length, road type, estimated driving speed, and estimated vehicle arrival time. Based on the road condition information, road condition impact features related to active air dam opening adjustment are extracted, constructing an opening-related road condition impact feature set. This set includes future speed change trend features, road slope change features, and road load change features. For the future speed change trend features, the speed change amplitude is calculated based on the difference between the current vehicle speed and the navigation-predicted speed, and a preset speed influence threshold is used to determine the speed trend influence amount. When the predicted speed is higher than the current speed and the speed change amplitude exceeds the preset threshold, the speed trend influence amount is increased. When the speed is lower than the current speed, the influence of the speed trend is reduced; the speed trend correction coefficient is obtained by normalizing the influence of the speed trend with a preset speed influence range; for the road slope change characteristics, the slope change gradient is calculated based on the continuous road slope sampling points in the navigation path, and the influence of the slope section on the air dam opening requirement is determined in combination with the current vehicle load status; when the vehicle enters an uphill section and the slope increases, the slope load influence is increased, and when the vehicle enters a downhill section or a gentle section, the slope load influence is reduced; the slope load correction coefficient is obtained by normalizing the slope load influence with a preset slope influence range; the initial air dam opening benchmark value is corrected based on the speed trend correction coefficient and the slope load correction coefficient to obtain the air dam opening pre-adjustment target value, and the specific calculation method is as follows: ,in, This indicates the pre-adjustment target value for the air dam opening. This represents the initial air dam opening reference value. This represents the vehicle speed trend correction coefficient. This represents the slope load correction factor. This indicates the weighting of vehicle speed trends. This indicates the weight of the slope load effect; based on the calculated air dam opening pre-adjustment target value, an active air dam pre-adjustment control target is generated so that the active air dam completes the opening adjustment before the vehicle enters the corresponding road condition.

[0021] Obtain the current actual opening degree of the air dam, and calculate the opening difference between the current actual opening degree of the air dam and the pre-adjustment target value of the air dam opening degree.

[0022] Specifically, the process involves: acquiring an opening detection signal set on the actuator of the target active air dam, wherein the opening detection signal includes at least one of the following: air dam expansion / contraction displacement detection value, rotation angle detection value, or actuator position feedback value; determining the actual opening position of the active air dam based on the opening detection signal, and performing zero-point calibration and range conversion processing on the actual opening position to convert it into a unified opening parameter, thereby obtaining the current actual opening value of the air dam; acquiring the air dam opening pre-adjustment target value determined in the previous steps, and calculating the difference between the current actual opening value of the air dam and the air dam opening pre-adjustment target value to obtain the opening difference value; wherein the formula for calculating the opening difference value is: ,in, This indicates the difference in air dam opening. This indicates the pre-adjustment target value for the air dam opening; This indicates the current actual opening degree of the air dam.

[0023] A tiered low-power opening adjustment strategy library is constructed, and the opening difference is adapted and tiered low-power adjustment is performed based on the tiered low-power opening adjustment strategy library.

[0024] Specifically, data on opening deviation, target opening change, actuator drive current, and adjustment completion time are collected during the historical adjustment process of the active air dam. Based on the correlation between the opening deviation data and the corresponding execution energy consumption data, a correspondence between opening deviation and adjustment energy consumption is established. The historical adjustment process is segmented and statistically analyzed according to the opening deviation range to determine the optimal adjustment step size, single adjustment duration, and adjustment interval for different deviation ranges. The absolute value of the opening deviation is divided into small deviation, medium deviation, and large deviation ranges. When the absolute value of the opening deviation is less than a first preset deviation threshold, the corresponding small deviation adjustment parameters are determined. The small deviation adjustment parameters include a smaller opening adjustment step size and a longer adjustment interval time, used to reduce frequent actuator movements; when the absolute value of the opening deviation is between the first preset deviation threshold and the second preset deviation threshold, the corresponding medium deviation adjustment parameter is determined, and the target opening change is divided into multiple continuous adjustment stages; when the absolute value of the opening deviation is greater than the second preset deviation threshold, the corresponding large deviation adjustment parameter is determined, increasing the single opening adjustment step size, so that the active air dam can quickly approach the target opening; the adjustment step size, number of adjustment stages, adjustment interval time, and execution power constraint parameters corresponding to each deviation interval are associated and stored to form a tiered low-power opening adjustment strategy library.

[0025] Furthermore, the adaptive strategy matching and tiered low-power adjustment of the opening difference based on the tiered low-power opening adjustment strategy library includes: Based on the aforementioned tiered low-power opening adjustment strategy library, strategy matching is performed on the opening difference to determine an adaptive dam opening adjustment strategy; the adaptive dam opening adjustment strategy is used to perform stage analysis on the opening difference to obtain staged opening adjustment parameters; and low-power closed-loop adjustment is performed on the target active dam based on the staged opening adjustment parameters.

[0026] Preferably, the deviation amplitude and deviation direction corresponding to the opening difference are obtained, and the absolute value of the opening difference is matched with the preset deviation level range in the tiered low-power opening adjustment strategy library. The tiered low-power opening adjustment strategy library is constructed based on the opening deviation, adjustment step size, actuator drive power, and adjustment response time collected during historical active air dam adjustment processes. The tiered low-power opening adjustment strategy library stores adjustment step size coefficients, stage division numbers, and stage execution time parameters corresponding to different deviation levels. An adaptive air dam opening adjustment strategy is determined according to the deviation level range in which the opening difference is located, and the opening adjustment direction or closing adjustment direction of the active air dam actuator is determined according to the direction of the opening difference. The adaptive air dam opening adjustment strategy is used to perform stage analysis on the opening difference, and the opening difference is calculated in stages according to the stage division number N corresponding to the adaptive air dam opening adjustment strategy to obtain the opening adjustment amount corresponding to each stage. The specific calculation method is as follows: ,in, This represents the opening adjustment amount in the i-th stage. This represents the difference between the current actual opening of the air dam and the pre-adjustment target value, where N represents the number of stages and i represents the current adjustment stage number. The target opening for each stage is calculated based on the stage opening adjustment amount, specifically as follows: ,in, This represents the target opening degree for stage i. This indicates the current actual opening degree of the air dam. Based on the target opening degree, the stage opening degree adjustment amount, and the stage execution time, stage opening degree adjustment parameters are generated. The target active air dam actuator is then controlled to perform stage-by-stage adjustments according to these parameters. After completing the i-th stage adjustment, the current actual air dam opening degree is re-acquired, and the remaining opening degree deviation is calculated based on the adjusted actual opening degree. The specific calculation method is as follows: ,in, Indicates the remaining opening deviation. This indicates the pre-adjustment target value for the air dam opening. This represents the actual dam opening after the i-th stage of adjustment is completed. Based on the relationship between the remaining opening deviation and the preset stability threshold, it is determined whether to continue the subsequent stage of adjustment. When the remaining opening deviation is greater than the preset stability threshold, the next stage opening adjustment parameters are called for adjustment. When the remaining opening deviation is less than or equal to the preset stability threshold, the driving power of the active dam actuator is reduced and the current opening state is maintained, thereby realizing low-power closed-loop adjustment of the active dam based on opening feedback.

[0027] Furthermore, the adaptive dam opening adjustment strategy is used to perform staged analysis on the opening difference to obtain staged opening adjustment parameters, including: The adaptive dam opening adjustment strategy is decomposed into phased targets to determine phased opening adjustment interval targets; the operating constraints of the target active dam opening adjustment device are obtained; the opening difference is analyzed in stages according to the phased opening adjustment interval targets and the operating constraints of the opening adjustment device to obtain phased opening adjustment parameters.

[0028] Preferably, based on the adjustment level corresponding to the adaptive dam opening adjustment strategy and the current opening difference, the number of stages and the stage adjustment ratio corresponding to the current adjustment process are determined, and the opening difference is target-splitting is performed according to the stage adjustment ratio to obtain multiple consecutive staged opening adjustment interval targets; wherein, the opening difference is denoted as... Let N be the number of stages. Then, the target adjustment amount for stage i is calculated as follows: ,in, This represents the opening adjustment amount in the i-th stage. Let represent the adjustment ratio in the i-th stage, and satisfy: The target opening for each stage is determined based on the opening adjustment amount at each stage. The calculation method is as follows: ,in, This represents the target opening degree for stage i. This indicates the current actual opening degree of the air dam. This represents the opening adjustment amount for stage j. Further, the operating constraints of the target active air dam's opening adjustment device are obtained, including the maximum single adjustment stroke, maximum adjustment speed, allowable drive power of the actuator, and continuous action interval time. The opening adjustment amount for each stage is constrained and corrected according to these operating constraints. When the calculated stage opening adjustment amount exceeds the maximum single adjustment stroke, the excess is allocated to subsequent adjustment stages. When the stage adjustment speed exceeds the maximum adjustment speed, the stage execution time is recalculated based on the maximum adjustment speed. The calculation method is as follows: ,in, Indicates the execution time of the i-th stage. This represents the absolute value of the opening adjustment in the i-th stage. This indicates the maximum allowable adjustment speed of the opening adjustment device; furthermore, based on the constrained stage target opening, stage adjustment amount, and stage execution time, a stage opening adjustment parameter is generated, and the stage opening adjustment parameter is used as the stage control basis of the target active air dam actuator to realize the gradual completion of opening adjustment according to the preset stage target.

[0029] Furthermore, the low-power closed-loop regulation of the target active air dam based on the phased opening adjustment parameters includes: The target active air dam is adjusted and monitored based on the adaptive air dam opening adjustment strategy to obtain the opening deviation feedback parameter; a PID controller is used to perform low-power closed-loop adjustment of the target active air dam based on the opening deviation feedback parameter.

[0030] Preferably, the target opening degree corresponding to the current adjustment stage is determined according to the adaptive air dam opening adjustment strategy, and the opening adjustment device of the target active air dam is controlled to perform stage adjustment actions according to the staged opening adjustment parameters; during the adjustment process, the current actual opening degree of the air dam is collected in real time by the opening degree detection device set on the active air dam actuator, and the current actual opening degree is compared with the target opening degree corresponding to the current stage to calculate the stage opening degree deviation feedback parameter. The specific calculation method is as follows: ,in, This represents the opening deviation feedback parameter at time k; This represents the target opening degree at time k. This represents the actual opening degree of the active air dam acquired at time k. Further, the opening degree deviation feedback parameter is input into the PID controller, and the control output for the current stage is calculated based on the proportional control term, integral control term, and derivative control term to obtain the actuator control quantity. The specific calculation method is as follows: ,in, This represents the control output of the actuator at time k; Indicates the proportional control coefficient; Indicates the integral control coefficient; Represents the differential control coefficient; Indicates the control sampling period; This represents the opening deviation feedback parameter at time j. The operating current or drive pulse width of the active air dam drive motor is adjusted according to the actuator control quantity output by the PID controller, so that the actual opening of the active air dam gradually approaches the stage target opening. During the PID closed-loop adjustment process, the control output amplitude is dynamically adjusted according to the opening deviation feedback parameter. When the opening deviation feedback parameter is large, the adjustment output is increased to shorten the time to reach the target opening. When the opening deviation feedback parameter enters the preset stable range, the drive output is reduced to reduce actuator energy consumption. Furthermore, by limiting the maximum drive power, reducing unnecessary integral accumulation, and maintaining a stable opening state, the actuator is prevented from continuously operating at high power. After completing the current stage adjustment, the current actual opening is used as the feedback input for the next stage adjustment. Stage target updates, deviation calculations, and PID control output adjustments are continuously executed until the current actual opening reaches the pre-adjustment target value of the air dam opening, thus achieving low-power closed-loop adjustment of the target active air dam based on stage feedback.

[0031] In summary, the embodiments of this application have at least the following technical effects: First, a sensor network is deployed around the target active airdam to collect multimodal driving environment data. This data is then categorized by operating condition to determine the current driving environment. Next, navigation traffic data is used to analyze the airdam opening requirement under the current driving environment, yielding a pre-adjustment target value. Then, the actual airdam opening is obtained, and the difference between the actual opening and the pre-adjustment target value is calculated. Finally, a tiered low-power opening adjustment strategy library is constructed. Based on this library, adaptive strategy matching and tiered low-power adjustment are performed on the opening difference. This solves the technical problems in existing technologies where the active airdam adjustment process struggles to adapt to dynamically changing driving environments, leading to insufficient accuracy and high energy consumption. It achieves the technical effect of adaptive adjustment of the active airdam opening based on environmental changes while reducing energy consumption during the adjustment process.

[0032] Example 2 is based on the same inventive concept as the low-power active air dam regulation method based on environmental changes in the foregoing examples, such as... Figure 2 As shown, this application provides a low-power active air dam regulation system based on environmental changes, wherein the system includes: Data acquisition module 11: Deploys a sensor network around the target active air dam, collects multimodal driving environment data through the sensor network, classifies the multimodal driving environment data according to operating conditions, and determines the current driving environment operating conditions; Demand analysis module 12: Combines navigation road condition data to perform air dam opening demand analysis on the current driving environment operating conditions, and obtains the air dam opening pre-adjustment target value; Calculation module 13: Obtains the current actual air dam opening, and calculates the opening difference between the current actual air dam opening and the air dam opening pre-adjustment target value; Matching and adjustment module 14: Constructs a tiered low-power opening adjustment strategy library, and performs adaptive strategy matching and tiered low-power adjustment on the opening difference based on the tiered low-power opening adjustment strategy library.

[0033] Furthermore, the data acquisition module 11 is used to perform the following methods: The multimodal driving environment data is time-axis aligned and cleaned to obtain usable multimodal driving environment data. A set of driving-related operating condition features is extracted from the usable multimodal driving environment data. The types of the set of driving-related operating condition features include driving features, thermal management features, and environmental features. Preset driving operating condition boundary rules are used to classify the driving-related operating condition features to determine the current driving environment operating condition.

[0034] Furthermore, the requirements analysis module 12 is used to perform the following methods: Based on historical opening experience data of active air dams, an air dam operating condition benchmark opening library is constructed; the air dam operating condition benchmark opening library is used to match the opening requirements of the current driving environment to obtain an initial air dam opening benchmark value; the initial air dam opening benchmark value is dynamically adjusted in conjunction with navigation road condition data to obtain the air dam opening pre-adjustment target value.

[0035] Furthermore, the requirements analysis module 12 is used to perform the following methods: Extract the set of road condition influence features related to the opening degree corresponding to the navigation road condition data; perform an influence degree analysis on the set of road condition influence features related to the opening degree to generate a vehicle speed trend correction coefficient and a slope load correction coefficient; dynamically adjust the initial air dam opening benchmark value based on the vehicle speed trend correction coefficient and the slope load correction coefficient to obtain the air dam opening pre-adjustment target value.

[0036] Furthermore, the matching adjustment module 14 is used to perform the following method: Based on the aforementioned tiered low-power opening adjustment strategy library, strategy matching is performed on the opening difference to determine an adaptive dam opening adjustment strategy; the adaptive dam opening adjustment strategy is used to perform stage analysis on the opening difference to obtain staged opening adjustment parameters; and low-power closed-loop adjustment is performed on the target active dam based on the staged opening adjustment parameters.

[0037] Furthermore, the matching adjustment module 14 is used to perform the following method: The adaptive dam opening adjustment strategy is decomposed into phased targets to determine phased opening adjustment interval targets; the operating constraints of the target active dam opening adjustment device are obtained; the opening difference is analyzed in stages according to the phased opening adjustment interval targets and the operating constraints of the opening adjustment device to obtain phased opening adjustment parameters.

[0038] Furthermore, the matching adjustment module 14 is used to perform the following method: The target active air dam is adjusted and monitored based on the adaptive air dam opening adjustment strategy to obtain the opening deviation feedback parameter; a PID controller is used to perform low-power closed-loop adjustment of the target active air dam based on the opening deviation feedback parameter.

[0039] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A low-power active air dam regulation method based on environmental changes, characterized in that, The method includes: A sensor network is deployed around the target active air dam to collect multimodal driving environment data. The multimodal driving environment data is then classified according to operating conditions to determine the current driving environment operating conditions. By combining navigation traffic data, an air dam opening demand analysis is performed on the current driving environment to obtain the air dam opening pre-adjustment target value. Obtain the current actual opening degree of the air dam, and calculate the opening difference between the current actual opening degree of the air dam and the pre-adjustment target value of the air dam opening; A tiered low-power opening adjustment strategy library is constructed, and the opening difference is adapted and tiered low-power adjustment is performed based on the tiered low-power opening adjustment strategy library.

2. The low-power active air dam regulation method based on environmental changes as described in claim 1, characterized in that, The multimodal driving environment data is classified according to operating conditions to determine the current driving environment operating conditions, including: The multimodal driving environment data is time-axis aligned and cleaned to obtain usable multimodal driving environment data; Extract the driving-related operating condition feature set of the available multimodal driving environment data, wherein the types of the driving-related operating condition feature set include driving features, thermal management features, and environmental features; Preset driving condition boundary rules are used to classify the driving-related condition feature set and determine the current driving environment condition.

3. The low-power active air dam regulation method based on environmental changes as described in claim 1, characterized in that, By combining navigation traffic data with the current driving environment conditions, an air dam opening demand analysis is performed to obtain the pre-adjustment target value for the air dam opening, including: Based on historical opening experience data of active gas dams, a benchmark opening database for gas dam operating conditions is constructed. The air dam operating condition benchmark opening degree library is used to match the opening degree requirement of the current driving environment to obtain the initial air dam opening degree benchmark value; By combining the navigation traffic data, the initial air dam opening benchmark value is dynamically adjusted to obtain the air dam opening pre-adjustment target value.

4. The low-power active air dam regulation method based on environmental changes as described in claim 3, characterized in that, The initial air dam opening benchmark value is dynamically adjusted based on the navigation traffic data to obtain the air dam opening pre-adjustment target value, including: Extract and obtain the set of traffic condition influence features related to the degree of openness corresponding to the navigation traffic data; An impact degree analysis is performed on the set of road condition influence characteristics related to the opening degree, and a vehicle speed trend correction coefficient and a slope load correction coefficient are generated. The initial air dam opening benchmark value is dynamically adjusted based on the vehicle speed trend correction coefficient and the slope load correction coefficient to obtain the air dam opening pre-adjustment target value.

5. The low-power active air dam regulation method based on environmental changes as described in claim 1, characterized in that, Based on the aforementioned tiered low-power opening adjustment strategy library, adaptive strategy matching and tiered low-power adjustment are performed on the opening difference, including: Based on the aforementioned tiered low-power opening adjustment strategy library, strategy matching is performed on the opening difference to determine an adaptive air dam opening adjustment strategy. The adaptive dam opening adjustment strategy is used to perform staged analysis on the opening difference to obtain staged opening adjustment parameters; The target active air dam is subjected to low-power closed-loop regulation based on the phased opening adjustment parameters.

6. The low-power active air dam regulation method based on environmental changes as described in claim 5, characterized in that, The adaptive dam opening adjustment strategy is used to perform staged analysis on the opening difference to obtain staged opening adjustment parameters, including: The adaptive air dam opening adjustment strategy is broken down into phased objectives to determine the phased opening adjustment interval objectives. Obtain the operating constraints of the target active air dam's opening adjustment device; Based on the target of the phased opening adjustment interval and the operating constraints of the opening adjustment device, the opening difference is analyzed for phased adjustment to obtain the phased opening adjustment parameters.

7. The low-power active air dam regulation method based on environmental changes as described in claim 5, characterized in that, Low-power closed-loop regulation of the target active air dam based on the phased opening adjustment parameters includes: The target active air dam is adjusted and monitored based on the adaptive air dam opening adjustment strategy to obtain opening deviation feedback parameters; A PID controller is used to perform low-power closed-loop regulation of the target active air dam based on the opening deviation feedback parameters.

8. A low-power active air dam regulation system based on environmental changes, characterized in that, For implementing the low-power active air dam regulation method based on environmental changes as described in any one of claims 1-7, the system comprises: Data acquisition module: Deploy a sensor network around the target active air dam, collect multimodal driving environment data through the sensor network, classify the multimodal driving environment data according to operating conditions, and determine the current driving environment operating conditions; Demand Analysis Module: Combines navigation traffic data to perform air dam opening demand analysis on the current driving environment, and obtains the pre-adjustment target value of air dam opening; Calculation module: Obtains the current actual opening degree of the air dam, and calculates the opening difference between the current actual opening degree of the air dam and the pre-adjustment target value of the air dam opening; Matching and Adjustment Module: Constructs a tiered low-power opening adjustment strategy library, and performs adaptive strategy matching and tiered low-power adjustment on the opening difference based on the tiered low-power opening adjustment strategy library.

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