A multi-system collaborative wind resistance and wind noise dynamic optimization method and system

Abstract

This invention belongs to the field of new energy vehicle technology, specifically relating to a multi-system collaborative dynamic optimization method and system for wind resistance and wind noise. It is used for the collaborative optimization of wind resistance and wind noise in high-speed driving conditions of new energy vehicles. The method includes: acquiring vehicle driving status data and in-vehicle wind noise values, and determining optimization priorities based on the current driving mode and driving scenario. The optimization priorities characterize the relative order between wind resistance optimization targets and wind noise optimization targets. Based on the optimization priorities, chassis attitude parameters and aerodynamic component parameters are used as the quantities to be optimized for collaborative solution to obtain target values ​​for each quantity to be optimized. The corresponding actuators are controlled according to the target values, and in-vehicle wind noise values ​​and wind resistance characteristics are collected after the actuators are activated. Based on the collected in-vehicle wind noise values ​​and wind resistance characteristics, the target values ​​for each quantity to be optimized are dynamically corrected, and the optimization priorities are redefined when the driving mode or driving scenario changes.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle technology, specifically relating to a multi-system collaborative dynamic optimization method and system for wind resistance and wind noise. Background Technology

[0002] In existing technologies, the optimization of high-speed vehicle operation is scattered across four relatively independent areas: aerodynamics, chassis, platooning, and wind noise suppression. Active grille shutters rely solely on temperature thresholds for engine cooling or battery thermal management for opening and closing, neglecting wind noise characteristics and real-time aerodynamic drag parameters. Air suspension's height and attitude adjustments are entirely based on dynamic requirements, failing to account for the impact of pitch and roll on aerodynamic drag and lift, and are not linked to variable aerodynamic accessories. Platooning, prioritizing driving safety and traffic efficiency, only uses following distance as a constraint, without actively utilizing the low-pressure wake of the preceding vehicle to reduce drag for the following vehicle, nor addressing the noise transmission from wake turbulence pulsations to the side windows and exterior mirrors of the following vehicle. Wind noise suppression is limited to passive sound insulation such as sound-absorbing cotton, sound-insulating felt, and sealing strips, as well as active noise reduction within the vehicle, all of which involve propagation path suppression or sound response cancellation, without intervening at aerodynamic sources such as turbulent pressure pulsations on the vehicle surface, separation flow impacts, and cavity resonance.

[0003] The systemic defects of the above technologies are as follows: First, the optimization goals of wind resistance and wind noise are disconnected and contradictory. The low-drag design exacerbates the vortex intensity of the A-pillar and the airflow velocity gradient near the side windows, which in turn increases the wind noise inside the vehicle. In high-speed autonomous driving scenarios, the occupants' sensitivity to the acoustic environment is about 30% higher than that of manual driving. Second, the chassis and aerodynamic control lack cross-domain coordination and cannot dynamically adjust the vehicle attitude based on real-time information such as vehicle speed, crosswind, and road slope to achieve global aerodynamic optimization. Furthermore, no model for predicting the drag and noise of the following vehicle based on the wake structure of the preceding vehicle has been established for platooning. Third, the noise reduction methods are inherently limited. Reducing the wind noise inside the vehicle by 1 decibel requires adding several kilograms to more than ten kilograms of sound insulation material. It is almost powerless for frequencies above 500Hz. The active noise reduction bandwidth only covers frequencies below 200Hz. Moreover, it does not conduct aerodynamic styling or flow control optimization for specific wind noise sources such as rearview mirror trails, A-pillar separation bubbles, and door handle step gaps. Summary of the Invention

[0004] To overcome the shortcomings of the existing technology, this invention proposes a multi-system collaborative dynamic optimization method and system for wind resistance and wind noise, which aims to optimize wind resistance and wind noise at high speeds while being compatible with platooning and chassis attitude adjustment.

[0005] A first aspect of this invention provides a method for dynamic optimization of wind resistance and wind noise through multi-system collaboration, the method comprising: The vehicle's driving status data and in-vehicle wind noise value are acquired, and the optimization priority is determined based on the current driving mode and driving scenario. The optimization priority is used to characterize the relative order between the wind resistance optimization target and the wind noise optimization target. Based on the optimization priority, chassis attitude parameters and aerodynamic component parameters are used as the optimization quantities to be solved collaboratively to obtain the target values ​​of each of the optimization quantities; wherein, the chassis attitude parameters include at least one of vehicle height and suspension stiffness, and the aerodynamic component parameters include at least one of underbody protection plate opening, air intake grille opening, and exterior rearview mirror angle; Control the corresponding actuator to move according to the target value, and collect the in-vehicle wind noise value and wind resistance value after the actuator moves; The target values ​​of each of the quantities to be optimized are dynamically corrected based on the collected in-vehicle wind noise value and wind resistance value, and the optimization priority is re-determined when the driving mode or the driving scenario is switched.

[0006] In some embodiments, determining the optimization priority based on the current driving mode and driving scenario includes: Based on the validity of the vehicle-to-vehicle coordination data in the driving status data, the driving scenario is determined to be either a platoon driving scenario or a single-vehicle driving scenario; In response to the driving mode being manual driving mode, the order of the optimization priorities—safety target, heat dissipation target, wind resistance optimization target, and wind noise optimization target—is determined to decrease sequentially. In response to the driving mode being autonomous driving mode and the driving scenario being a single-vehicle driving scenario, the order of the optimization priority targets—safety target, heat dissipation target, wind noise optimization target, and wind resistance optimization target—is determined to decrease sequentially. In response to the driving mode being autonomous driving mode and the driving scenario being platooning driving scenario, the order of the safety target, platooning energy efficiency target and wind noise optimization target in the optimization priority is determined to decrease sequentially, and the platooning energy efficiency target includes the benefit item of the wind resistance optimization target; The driving status data includes environmental perception data, vehicle status data, and vehicle-to-vehicle cooperative data.

[0007] In some embodiments, before determining the optimization priority based on the current driving mode and driving scenario, the method further includes: Safety risks are identified based on the driving status data, and the safety risks include at least one of collision risk, water wading risk, and core component overheating risk; In response to the identification of the security risk, a security control strategy corresponding to the identified security risk is executed, and the current collaborative solution is skipped; In response to the failure to identify the aforementioned safety risk, the step of determining the optimization priority based on the current driving mode and driving scenario is executed; The safety constraint threshold satisfied by the collaborative solution changes with the driving scenario: when the driving scenario is a platoon driving scenario, the safety constraint threshold is determined by vehicle-to-vehicle cooperative braking; when the driving scenario is a single-vehicle driving scenario, the safety constraint threshold is determined by single-vehicle braking.

[0008] In some embodiments, the step of collaboratively solving for chassis attitude parameters and aerodynamic component parameters as optimization quantities based on the optimization priority to obtain the target value of each optimization quantity includes: According to the optimization priority, weight coefficients and constraint thresholds are configured for each optimization objective. Among them, the safety objective and the heat dissipation objective are entered into the collaborative solution in the form of the constraint thresholds. The remaining optimization objectives are configured with the weight coefficients, and the higher the order of the optimization objective, the larger the weight coefficients are. The sum of all the weight coefficients is 1. Using the minimum weighted sum of the objective functions of each of the optimization objectives as the optimization criterion, the solution is obtained within the feasible region that satisfies each of the constraint thresholds, resulting in at least two sets of candidate parameter combinations. The optimization criterion is expressed as follows: Based on the comprehensive performance score, the optimal combination of the at least two candidate parameters is selected, and the value of each of the quantities to be optimized in the candidate parameter combination with the highest comprehensive performance score is determined as the target value. The overall performance score is expressed as follows: In the formula, , , The collaborative solution stage involves combining parameters based on a pre-defined model. The predicted values ​​of drag coefficient, in-vehicle wind noise, and energy consumption are obtained. , , These represent the baseline values ​​for drag coefficient, in-vehicle wind noise, and energy consumption under the same operating conditions without collaborative solving. This represents the maximum adjustable level of wind noise inside the vehicle. These are the weighting coefficients for wind resistance optimization target, wind noise optimization target, and energy efficiency benefit item, respectively.

[0009] In some embodiments, when the quantity to be optimized includes vehicle height, the target value of the vehicle height is determined by the following formula: In the formula, The target value for the vehicle body height. Standard vehicle height This represents the maximum vehicle height adjustment range corresponding to the current driving scenario. This is the vehicle speed height adjustment coefficient. This is the wind noise height adjustment coefficient. Current vehicle speed Threshold for vehicle speed activation This represents the current wind noise level inside the vehicle. This is the wind noise compensation threshold; Among them, only Not less than If there are no bumpy or uphill sections within the preset distance ahead, the target value for the vehicle height is determined; otherwise, the vehicle height is maintained at [value missing]. .

[0010] In some embodiments, the vehicle speed height adjustment coefficient With the wind noise height adjustment coefficient All values ​​are determined by multiplying the base value by the scene correction factor, and: In response to the increasing order of the wind noise optimization objective in the optimization priority, the [efforts] are increased. Scene correction coefficient; In response to the increasing order of wind resistance optimization target or formation energy efficiency target in the optimization priority, the [efficiency / reduction] is increased. Scene correction coefficient; In response to the tightening of the constraint threshold corresponding to the safety objective in the optimization priority, while reducing and The scene correction coefficient is reduced. .

[0011] In some embodiments, after acquiring the vehicle's driving status data and the in-vehicle wind noise value, the method further includes: Wind noise signals at different locations are collected using multiple external microphones placed at different positions on the vehicle body. The wind noise signals of each channel are converted into sound power, and the proportion of the sound power of each channel in the total sound power is calculated to obtain the contribution of the wind noise source at each location. The wind noise source with the highest contribution and greater than the preset contribution threshold is identified as the target wind noise source; In the collaborative solution, the adjustment weight of the optimization quantity corresponding to the target wind noise source in the objective function of the wind noise optimization objective is increased; wherein, the optimization quantity corresponding to the target wind noise source includes at least one of the underbody panel opening and the exterior rearview mirror angle.

[0012] In some embodiments, when the driving scenario is a platooning scenario, the quantity to be optimized also includes the following distance, and the target value of the following distance is not less than the safe following distance; The safe following distance is determined by the following formula: In the formula, The safe following distance is... This is the lower limit of the wake following distance. Current vehicle speed This refers to the total response time from when the vehicle receives the vehicle-to-vehicle cooperative data to when the braking system builds up pressure. This is the maximum braking deceleration of the vehicle. The maximum braking deceleration of the preceding vehicle is obtained through the vehicle-to-vehicle cooperative data. For safety redundancy distance.

[0013] In some embodiments, when the driving scenario is a platooning driving scenario, the collaborative solution further includes: The pneumatic component parameters of each vehicle are solved in stages according to the order of the vehicles in the formation, so that the underbody opening of the vehicle with the first order is not greater than the underbody opening of the vehicle with the last order. Calculate the formation energy efficiency score, and when the formation energy efficiency score is lower than the preset score threshold, adjust the initial value of the underbody protection plate opening of each vehicle or relax the upper limit of the underbody protection plate opening of the vehicle with the later order, and re-execute the graded solution until the formation energy efficiency score is not lower than the preset score threshold. The formation energy efficiency score is a weighted sum of average energy consumption ratio, aerodynamic benefits, energy recovery efficiency and collaborative synchronization loss. The values ​​of each item are limited to between 0 and 1, and the weights of each item decrease sequentially.

[0014] A second aspect of this invention provides a multi-system collaborative dynamic optimization system for wind resistance and noise, the system comprising: The priority decision module is configured to acquire vehicle driving status data and in-vehicle wind noise value, and determine optimization priority based on the current driving mode and driving scenario. The optimization priority is used to characterize the relative order between the wind resistance optimization target and the wind noise optimization target. The parameter solving module is configured to perform collaborative solving based on the optimization priority, using chassis attitude parameters and aerodynamic component parameters as the quantities to be optimized, to obtain the target values ​​of each of the quantities to be optimized; wherein, the chassis attitude parameters include at least one of vehicle height and suspension stiffness, and the aerodynamic component parameters include at least one of underbody protection plate opening, air intake grille opening, and exterior rearview mirror angle; The control and feedback module is configured to control the corresponding actuator action according to the target value, and to collect the in-vehicle wind noise value and wind resistance value after the actuator action; The dynamic correction module is configured to dynamically correct the target values ​​of each of the quantities to be optimized based on the collected in-vehicle wind noise value and the wind resistance characterization value, and to re-determine the optimization priority when the driving mode or the driving scenario is switched.

[0015] The beneficial effects of this invention include: Overcoming the inherent conflict between wind resistance and wind noise optimization, achieving a synergistic improvement in economy and comfort in high-speed autonomous driving scenarios; through multi-system linkage and vehicle-to-vehicle collaboration, constructing a full-chain optimization system of perception-decision-execution-closed loop, simultaneously meeting multiple needs such as wind resistance reduction, wind noise suppression, energy saving and driving safety. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments are briefly introduced below; the accompanying drawings described below are only some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a multi-system collaborative dynamic optimization method for wind resistance and noise, as shown in an embodiment of the present invention. Figure 2 This is a flowchart illustrating step S1 in an embodiment of the present invention; Figure 3 This is a flowchart illustrating another multi-system collaborative dynamic optimization method for wind resistance and noise, as shown in an embodiment of the present invention. Figure 4 This is a flowchart illustrating step S2 in an embodiment of the present invention; Figure 5 This is a flowchart illustrating another multi-system collaborative dynamic optimization method for wind resistance and noise, as shown in an embodiment of the present invention. Figure 6 This is another flowchart illustrating step S2 in an embodiment of the present invention; Figure 7 This is a structural block diagram of a multi-system collaborative wind resistance and wind noise dynamic optimization system shown in an embodiment of the present invention. Detailed Implementation

[0018] The concept, specific structure, and technical effects of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments.

[0019] like Figure 1 As shown in the figure, an embodiment of the present invention provides a multi-system collaborative dynamic optimization method for wind resistance and wind noise, the method comprising: S1. Obtain vehicle driving status data and in-vehicle wind noise value, and determine optimization priority based on the current driving mode and driving scenario. Optimization priority is used to characterize the relative order between wind resistance optimization target and wind noise optimization target.

[0020] Among them, driving status data is a set of data output by all sensors in the vehicle within the same control cycle, collectively representing the vehicle's operating status and the surrounding environment. The output frequencies of each sensor are as follows: 50Hz for the forward millimeter-wave radar and binocular camera, chassis inertial measurement unit and wheel speed sensor; 10Hz for the front-facing lidar and high-precision map, electric drive system sensors; and 20Hz for the suspension system sensors.

[0021] The in-vehicle wind noise value is a sound pressure level value, measured in decibels, obtained by collecting data from wind noise sensors located in the passenger compartment and then weighting the data. Since this sound pressure signal contains wind noise, tire noise, road noise, and motor noise, frequency band separation is first performed, and the mid-to-high frequency band above 500Hz is taken as the dominant frequency band for wind noise. Then, the remaining tire noise and road noise components are subtracted based on the current vehicle speed and road surface smoothness. The remaining components are then weighted using an A-weighting method to obtain the in-vehicle wind noise value.

[0022] Driving mode refers to the ownership of longitudinal and lateral control of the vehicle. When control belongs to the human driver, it is manual driving mode; when it belongs to the intelligent driving domain controller, it is automatic driving mode. Both driving modes can be further subdivided into Normal mode, Sport mode, and Comfort mode. These sub-modes do not change the ownership of control, but only change the relative order of the wind resistance optimization target and the wind noise optimization target: in Sport mode, the order of the wind resistance optimization target increases; in Comfort mode, the order of the wind noise optimization target increases; and in Normal mode, the order of the two is directly determined by the driving mode and the driving scenario.

[0023] Driving scenario refers to whether a stable vehicle-to-vehicle communication and collaborative control relationship has been established between the vehicle and surrounding vehicles traveling in the same direction. When this relationship is established, it is a platoon driving scenario; when this relationship is not established, it is a single-vehicle driving scenario.

[0024] The optimization priority is an ordered sequence used to specify the order in which safety objectives, heat dissipation objectives, drag optimization objectives, wind noise optimization objectives, and formation energy efficiency objectives are satisfied within the current control cycle. This sequence is set because, while reducing the vehicle height can reduce the frontal area and thus reduce drag, it also reduces the underbody airflow channel and increases the underbody airflow velocity, thereby increasing chassis wind noise. These two objectives are contradictory and cannot both be maximized simultaneously. It is necessary to first determine which one yields to which in order to obtain a unique solution in subsequent calculations.

[0025] S2. Based on the optimization priority, the chassis attitude parameters and aerodynamic component parameters are used as the quantities to be optimized and solved collaboratively to obtain the target values ​​of each quantity to be optimized. Among them, the chassis attitude parameters include at least one of vehicle height and suspension stiffness, and the aerodynamic component parameters include at least one of underbody protection plate opening, air intake grille opening and exterior rearview mirror angle.

[0026] Chassis attitude parameters are adjustable parameters that determine the vertical position and support characteristics of the vehicle body, including vehicle height and suspension stiffness. Vehicle height is changed by the inflation and deflation of air springs, adjustable within a range of 50mm above and below the standard height, with an adjustment accuracy of ±2mm. Suspension stiffness is changed by continuously damped adjustable shock absorbers, steplessly adjustable between 10N / mm and 30N / mm, with an adjustment response time of no more than 0.5 seconds. Aerodynamic component parameters refer to adjustable parameters that directly change the boundary conditions of the airflow field on the outer surface and underside of the vehicle body, including the opening of the underbody protection plate and the opening of the air intake grille.

[0027] The bottom guard plate opening is the angle at which the two sets of independent movable cover plates on the base bottom guard plate rotate around their hinge axis. It can be steplessly adjusted between 0° and 90°, with an action response time of no more than 1 second. The cover plate edge is equipped with sealing strips, and the inner side of the cover plate is lined with sound-absorbing cotton. Therefore, this angle simultaneously determines the bottom flow field shape and the amount of chassis noise absorbed.

[0028] The opening of the air intake grille determines the amount of air intake at the front, which in turn determines the heat dissipation capacity and the separation state of the airflow at the front.

[0029] The exterior rearview mirror angle is the deflection angle of the exterior rearview mirror housing relative to the longitudinal symmetry plane of the vehicle body. It is driven by the exterior rearview mirror attitude execution unit, and its adjustable range, adjustment accuracy and response time are determined according to the overall vehicle layout. This angle directly determines the intensity and shedding frequency of the mirror housing wake vortex, thus simultaneously affecting lateral aerodynamic drag and wind noise in the side window area.

[0030] In this embodiment, the adjustable quantities belonging to the chassis control system and the aerodynamic control system are placed in the same mathematical problem to determine their values ​​simultaneously, rather than being determined independently by each system and then simply superimposed; the target value is the expected value that each quantity to be optimized should achieve within the current control cycle, which is output by the collaborative solution.

[0031] S3. Control the corresponding actuators to move according to the target value, and collect the in-vehicle wind noise and wind resistance values ​​after the actuators move.

[0032] The actuator is the component that receives the target value and converts it into actual physical actions. This includes the body attitude actuator that drives the air springs and continuously damped adjustable shock absorbers, the chassis aerodynamic actuator that drives the movable cover and air intake grille, and the exterior rearview mirror attitude actuator that drives the deflection of the exterior rearview mirror housings. The decision controller converts the target value into position or current commands and sends them via the vehicle's Ethernet network. The actuator tracks this command in a closed loop until the actual value falls within the allowable deviation range, and then transmits the execution status back.

[0033] Aerodynamic drag is an observable measure that indirectly reflects the current aerodynamic drag level of the vehicle. Since the drag coefficient cannot be directly measured by onboard sensors, the aerodynamic drag is characterized by real-time energy consumption, drive motor output torque, and longitudinal deceleration during coasting at the same vehicle speed and gradient. Data is collected after the actuator has acted to obtain the actual effect of the control action rather than the model prediction effect.

[0034] S4. Based on the collected in-vehicle wind noise and wind resistance values, the target values ​​of each quantity to be optimized are dynamically corrected, and the optimization priority is redefined when the driving mode or driving scenario is switched.

[0035] The collected in-vehicle wind noise and wind resistance values ​​are subtracted from the predicted values ​​used in the collaborative solution. The difference is then fed back to the collaborative solution loop as a feedback quantity. In the next control cycle, an increment with the opposite sign to the difference is applied to the target value of each quantity to be optimized, so that the actual wind noise and actual wind resistance gradually converge to the desired level, thereby transforming the open-loop single solution into closed-loop control.

[0036] Re-determining optimization priorities means that when the driving mode is switched from manual driving mode to automatic driving mode, or the driving scenario is switched from single-vehicle driving scenario to platoon driving scenario, the original sequence no longer matches the current control ownership and coordination relationship. At this time, the dynamic correction based on the original sequence is terminated and the optimization priority is re-determined, so that the control flow constitutes a loop that self-reconstructs as the scenario changes.

[0037] Specifically, the vehicle cruises at 120 km / h on a highway. Within 10 milliseconds, the onboard Ethernet aggregates data from various sources to the decision controller: real-time vehicle speed 120 km / h, in-vehicle wind noise 58 dB, drive motor temperature 92 °C, no bumpy or uphill sections within 5 km ahead, and the vehicle-to-vehicle communication unit has not detected any vehicles traveling in the same direction with whom a cooperative relationship can be established. Based on this, the decision controller determines that the driving scenario is a single-vehicle driving scenario and sets the optimization priority as follows: safety objective, heat dissipation objective, wind noise optimization objective, and wind resistance optimization objective, decreasing in that order.

[0038] Subsequently, the decision controller uses vehicle height, suspension stiffness, underbody protection plate opening, and grille opening as the four factors to be optimized and solves them together to obtain the target value of vehicle height of 143mm, suspension stiffness of 14N / mm, underbody protection plate opening of 22°, and grille opening of 15% of full opening.

[0039] The vehicle attitude actuator and chassis aerodynamic actuator complete their actions within 1 second. The measured wind noise level inside the vehicle is 54 dB, real-time energy consumption decreases from 20.2 kWh / 100km to 19.2 kWh / 100km, and the drag coefficient decreases from 0.23 to 0.215. The decision controller calculates the difference between the measured 54 dB and the predicted 54.5 dB to obtain a -0.5 dB feedback value. Based on this, in the next control cycle, the target value for the underbody protection plate opening is increased by 1° and the target value for the vehicle height is decreased by 1 mm. When the vehicle subsequently establishes a cooperative control relationship with a vehicle traveling in the same direction ahead, the driving scenario switches to a platooning scenario. The decision controller terminates the current dynamic correction and returns to re-determine the optimization priority.

[0040] Specifically, the acquisition of driving status data and in-vehicle wind noise values ​​can be achieved by using a unified time-based aggregation of in-vehicle time-sensitive network Ethernet, or by periodic message reporting after local fusion of each domain controller; the determination of optimization priorities can be achieved by using a static mapping table or a finite state machine; the acquisition of wind resistance characteristics can be achieved by using real-time energy consumption moving average, or by using longitudinal deceleration back-calculation based on power-off coasting identification; the dynamic correction of target values ​​can be achieved by using a proportional-integral controller, or by using a recursive least squares method with a forgetting factor to update model parameters online.

[0041] The four actions in this embodiment are interconnected, forming a complete closed loop of perception, decision-making, execution, and feedback: wind noise is transformed from a subjective perception into an observable controllable variable, and it enters the same problem for solving as wind resistance. The inherent contradiction between the two is transformed into a sequencing problem with a definite order, allowing vehicle height and underbody protection plate opening to compensate for each other. Actual measurements show that this collaborative approach can achieve an additional 2dB of wind noise optimization compared to adjusting vehicle height alone, and an additional 3% wind resistance reduction compared to adjusting underbody protection plate alone. Dynamic corrections after execution ensure that the control results are independent of model accuracy, and the same process can cover three types of application scenarios.

[0042] In some embodiments, such as Figure 2 As shown, in step S1, the optimization priority is determined based on the current driving mode and driving scenario, including: S101. Based on the validity of the vehicle-to-vehicle coordination data in the driving status data, determine whether the driving scenario is a platoon driving scenario or a single-vehicle driving scenario.

[0043] The driving status data includes environmental perception data, vehicle status data, and vehicle-to-vehicle collaborative data.

[0044] Environmental perception data includes obstacle distance, obstacle type, and obstacle speed output by forward millimeter-wave radar and binocular camera; road surface smoothness, slope, curvature, and adhesion coefficient output by forward-facing lidar and high-precision map; ambient temperature, humidity, precipitation level, and wind speed and direction output by onboard environmental sensors; and road speed limits, traffic signs, and forward accident information output by roadside units and navigation system.

[0045] The vehicle status data includes real-time vehicle speed, acceleration, and longitudinal impact force output by the chassis inertial measurement unit and wheel speed sensors; drive motor temperature, power battery temperature, electronic control system temperature, and heat dissipation power output by the three-electric system sensors; vehicle height, suspension stiffness, and real-time values ​​of shock absorber damping output by the suspension system sensors; underbody protection plate opening, sealing pressure, and actuator status output by the underbody protection plate sensors; and real-time energy consumption, driving range, throttle opening, and braking pressure output by the vehicle controller.

[0046] Vehicle-to-vehicle (V2V) collaborative data refers to the real-time vehicle speed, acceleration, control commands, fault status, emergency signals, following distance, relative speed, and position offset received by the vehicle's V2V (Vehicle-to-Vehicle) communication unit via the 5G vehicle wireless communication module from adjacent vehicles traveling in the same direction.

[0047] The three types of data have clearly defined roles: vehicle-to-vehicle collaborative data is used to determine the driving scenario, the driving mode flag in the vehicle status data is used to determine the driving mode, and environmental perception data and vehicle status data together provide constraints and initial values ​​for subsequent collaborative solutions.

[0048] Validity refers to the data simultaneously meeting three conditions: first, the communication link is uninterrupted and the message reception cycle deviation is within the allowable range; second, the synchronization error between the message timestamp and the vehicle's time base does not exceed 1 millisecond; and third, the sending vehicle has completed platoon member authentication and is within a reliable communication distance of not less than 300 meters. When all three conditions are met, it is determined to be a platoon driving scenario; if any condition is not met, it is determined to be a single-vehicle driving scenario. Validity is used as the criterion because only when the data is valid can the vehicle know the maximum braking deceleration and braking intention of the vehicle in front.

[0049] S102. In response to the driving mode being manual driving mode, the order of the optimization priorities—safety target, heat dissipation target, wind resistance optimization target, and wind noise optimization target—is determined to decrease sequentially.

[0050] In manual driving mode, control belongs to the human driver, whose acceleration and braking operations are random and may involve intense driving conditions such as continuous rapid acceleration and sudden braking. Instantaneous heat generation cannot be predicted in advance. Therefore, heat dissipation targets are prioritized over wind resistance and wind noise optimization targets. The opening of the underbody protection plate and the air intake grille must first meet the requirement that the heat dissipation power is not less than 1.2 times the current heat generation power. This avoids closing the underbody protection plate in pursuit of low wind resistance, which could cause the drive motor temperature to exceed the safety threshold of 120°C. The priority of wind resistance optimization targets is higher than that of wind noise optimization targets because the driver's subjective sensitivity to wind noise is lower in this mode.

[0051] S103. In response to the driving mode being autonomous driving mode and the driving scenario being a single-vehicle driving scenario, the order of the optimization priorities—safety target, heat dissipation target, wind noise optimization target, and wind resistance optimization target—is determined to decrease sequentially.

[0052] In autonomous driving mode and single-vehicle driving scenario, control belongs to the intelligent driving domain controller. Based on high-precision maps and navigation routes, it can predict driving conditions over the next 5km to 10km and accurately estimate the trend of heat generation. There is no need to reserve excessive heat dissipation redundancy; the heat dissipation targets only need to meet the following constraints: drive motor temperature not exceeding 120℃, power battery temperature not exceeding 45℃, and electronic control system temperature not exceeding 85℃. In this mode, occupants are no longer in control of the vehicle, and their sensitivity to aerodynamic noise is increased by more than 30% compared to manual driving mode. Therefore, wind noise optimization is prioritized over wind resistance optimization, and the reduction in vehicle height will be actively reduced.

[0053] S104. In response to the driving mode being autonomous driving mode and the driving scenario being platooning, the order of safety target, platooning energy efficiency target and wind noise optimization target in the optimization priority is determined to decrease in that order, and the platooning energy efficiency target includes the benefit item of wind resistance optimization target.

[0054] In autonomous driving mode and platooning scenarios, the vehicle height, underbody protection opening, and following distance of each vehicle in the platoon are uniformly planned by the platooning coordination controller. The drag reduction of an individual vehicle is no longer an independent optimization objective. This is because the following vehicle travels within the low-pressure wake zone behind the preceding vehicle, and its drag reduction is determined by both the following distance and the aerodynamic attitude of the preceding vehicle. Individually reducing its own drag may disrupt the overall flow field structure of the platoon. The platooning energy efficiency target is a weighted average of four factors: average energy consumption ratio, aerodynamic benefits, energy recovery efficiency, and coordination and synchronization losses. Aerodynamic benefits refer to the reduction in the average drag coefficient of the platoon relative to the drag coefficient of an individual vehicle.

[0055] Specifically, a vehicle is driving in autonomous driving mode on a highway. The vehicle-to-vehicle communication unit detects a vehicle traveling in the same direction that has completed platoon member authentication 180m ahead. The message reception cycle is stable and the timestamp synchronization error is 0.4 milliseconds. Based on this, the driving scenario is determined to be a platoon driving scenario, and the optimization priority is determined to be safety target, platoon energy efficiency target, and wind noise optimization target, which decreases in that order.

[0056] When the convoy disbands due to the departure of the lead vehicle and the vehicle-to-vehicle communication unit fails to receive a valid message for three consecutive message cycles, the driving scenario switches to a single-vehicle driving scenario. The optimization priorities are redefined as safety, heat dissipation, wind noise optimization, and wind resistance optimization, decreasing in that order. If the driver takes over the steering wheel and presses the accelerator pedal at this time, the driving mode switches to manual driving mode, and the optimization priorities are adjusted again to safety, heat dissipation, wind resistance optimization, and wind noise optimization, decreasing in that order once more. The underbody protection plate opening is then increased from 22° to 35° to improve heat dissipation.

[0057] Among them, the validity of vehicle-to-vehicle cooperative data can be determined by setting a threshold for the synchronization error between the message reception period and the timestamp and verifying it frame by frame, or by constructing a communication quality evaluation function with the number of consecutive lost frames and the signal strength threshold; the identification of driving mode can be achieved by reading the control ownership flag, or by identifying the takeover intention by analyzing the steering wheel torque and pedal travel signals; the storage of optimization priorities can be achieved by using a two-dimensional lookup table, or by arranging them in descending order of priority scores; the inclusion of drag benefit in formation energy efficiency targets can be achieved by introducing the drag coefficient reduction in the aerodynamic benefit item, or by implicitly including the drag power ratio in the average energy consumption ratio item.

[0058] This embodiment transforms the optimization priority from a fixed configuration to a dynamic result jointly indexed by control ownership and collaborative relationships. Once communication deteriorates, it automatically reverts to a single-vehicle driving scenario. Real-world testing shows that with the heat dissipation target prioritized in manual driving mode, the highest temperature of the three-electric system can be controlled at 102℃ under 30 minutes of intense driving conditions, a reduction of 8℃ compared to the energy efficiency-first strategy, while energy consumption only increases by 2.3%. In platooning scenarios, replacing the individual vehicle aerodynamic drag optimization target with the platooning energy efficiency target further reduces overall aerodynamic drag by 4.2% and overall energy consumption by 2.8% compared to independent optimization for each vehicle.

[0059] In some embodiments, such as Figure 3 As shown, before determining the optimization priority based on the current driving mode and driving scenario, the process includes steps S01 to S03. These three steps are executed before step S1, and their numbers are only used to distinguish different steps and do not indicate the order of execution. S01. Based on driving status data, identify safety risks, including at least one of collision risk, water wading risk, and core component overheating risk.

[0060] Among them, safety risk identification refers to conducting an independent risk scan of the driving status data before determining the optimization priority, to determine whether there is a situation that endangers driving safety or component safety.

[0061] The collision risk is calculated by converting the distance to the obstacle and the relative speed output by the forward millimeter-wave radar and the binocular camera into the collision time. When the collision time is less than 3 seconds, a collision risk is determined to exist.

[0062] The risk of wading is detected by a multi-source sensing fusion module with an accuracy of ±2cm to determine the depth of water on the road ahead. When the water depth exceeds the safe wading depth when the underbody guard is closed, it is determined that there is a risk of wading.

[0063] The risk of overheating of core components is determined by the temperature output from the sensors of the three-electric system. When the temperature of the drive motor approaches 120°C, the temperature of the power battery approaches 45°C, or the temperature of the electronic control system approaches 85°C, it is determined that there is a risk of overheating of core components. This scan is placed before determining the optimization priority because the optimization of wind resistance and wind noise belongs to performance optimization, while safety is a bottom-line constraint. If the bottom line is not maintained, any performance optimization is meaningless.

[0064] S02. In response to the identification of a security risk, execute the security control strategy corresponding to the identified security risk and skip the current collaborative solution.

[0065] A security control strategy refers to deterministic actions that correspond one-to-one with the identified security risks.

[0066] When a collision risk is detected, braking is applied and the following distance is increased. At the same time, the vehicle height is increased and the suspension stiffness is increased to suppress brake dive, until the collision time is restored to more than 3 seconds.

[0067] When a risk of water immersion is detected, the two sets of movable covers on the bottom guard plate are completely closed, and the sealing strips on the edges of the covers form a waterproof seal with an IP67 rating, preventing water from entering the chassis.

[0068] When the risk of overheating of core components is identified, the opening of the bottom guard plate and the opening of the air intake grille are increased to the opening corresponding to a heat dissipation power of not less than 1.2 times the current heat generation power.

[0069] Skipping the current collaborative solution means that the optimization process will not be entered again within the same control cycle of the safety control strategy. Each quantity to be optimized will directly adopt the value given by the safety control strategy to avoid command conflicts with the safety action on the same actuator.

[0070] S03. In response to the lack of identified safety risks, perform steps to determine optimization priorities based on the current driving mode and driving scenario.

[0071] No safety risk is identified when the collision time is not less than 3 seconds, the water depth on the road ahead is lower than the safe wading depth, and the temperatures of the drive motor, power battery, and electronic control system are all below their respective safety thresholds. In this case, the vehicle is within the feasible region where optimization can be freely sought, and the control flow transitions to determining the optimization priority. This branch, together with the previous branch, forms a complete and mutually exclusive binary choice structure, ensuring that the control flow does not pause due to the intermediate state of the risk condition.

[0072] Among them, the safety constraint threshold satisfied by the collaborative solution changes with the driving scenario: when the driving scenario is a platoon driving scenario, the safety constraint threshold is determined by vehicle-to-vehicle cooperative braking; when the driving scenario is a single-vehicle driving scenario, the safety constraint threshold is determined by single-vehicle braking.

[0073] Safety constraint thresholds are the right-hand side terms of inequalities that must be satisfied during collaborative solving for optimization. The most critical term is the lower limit of following distance. This lower limit changes with the driving scenario because the physical nature of the braking process is different: In a single-vehicle driving scenario, the vehicle cannot know the braking capability and braking intention of the vehicle in front, and can only assume that the vehicle in front is momentarily stationary. Therefore, the lower limit is the sum of the vehicle's reaction displacement and the vehicle's complete braking distance. In a platoon driving scenario, the vehicle knows the maximum braking deceleration of the vehicle in front in real time and brakes synchronously with the vehicle in front. The vehicle in front also needs a braking distance. Therefore, the lower limit only needs to cover the difference between the vehicle's reaction displacement and the braking distances of the vehicles in front and behind.

[0074] Specifically, in an autonomous driving scenario where a vehicle is traveling at 100 km / h in platooning, the decision controller first performs safety risk identification in each control cycle. At this time, the forward millimeter-wave radar measures a distance of 20m from the vehicle in front, a relative speed of 0, an infinite collision time, no water accumulation on the road surface, and a drive motor temperature of 98℃. No safety risk is identified, and the control flow switches to determining optimization priorities and, based on vehicle-to-vehicle cooperative braking, determining a lower limit for the following distance of 15m. Cooperative problem-solving is then performed to find the optimal following distance above this lower limit, resulting in a target following distance of 18m.

[0075] When a vehicle cuts into the second lane ahead of the vehicle and reduces the collision time to 2.5 seconds, a collision risk is determined. The decision controller immediately sends a deceleration request to the braking system, increases the vehicle height from 140mm to 150mm and the suspension stiffness from 14N / mm to 22N / mm, and skips the current collaborative solution. The underbody protection plate opening and the grille opening remain unchanged from the previous cycle. After the cutting vehicle completes its lane change, the collision time returns to 3.4 seconds, and the safety risk is eliminated. If the vehicle in front moves away and vehicle-to-vehicle communication is interrupted, the minimum following distance is changed from 15m to 61.6m, determined by single-vehicle braking.

[0076] Among them, collision risk identification can be achieved by calculating the collision time from the obstacle distance and relative speed and comparing it with a 3-second threshold, or by intersecting the obstacle trajectory prediction with the planned trajectory of the vehicle and determining the intersection time; water wading risk identification can be achieved by using a binocular camera to perform image recognition of the water surface reflective texture, or by using millimeter-wave radar to identify the difference in echo intensity between the water surface and the road surface; skipping the current collaborative solution can be achieved by setting an enable bit and setting it to zero when a safety risk is identified, or by setting the safety target weight coefficient to 1 and the rest to 0; the safety constraint threshold can be achieved by using two sets of constraint expressions indexed by the scenario when the driving scenario changes, or by enabling and bypassing the braking capability item of the preceding vehicle based on the validity of the vehicle-to-vehicle collaborative data.

[0077] This embodiment elevates safety from an optimization objective parallel to wind resistance and wind noise to an entry criterion prior to optimization, ensuring that performance optimization can only be carried out within a confirmed safe and feasible domain. This avoids the waste of computational power and control delays associated with prior optimization followed by safety verification, and also eliminates the risk of safety actions being diluted by weighting. The safety constraint threshold switches according to the driving scenario, resolving the fundamental conflict between fixed safety distance and close platooning: if the lower limit is always determined by single-vehicle braking, the lower limit at 100km / h would be as high as 61.6m, preventing the platoon from entering the low-pressure area of ​​the preceding vehicle's wake; this mechanism compresses the lower limit to 15m while maintaining the braking safety margin, providing the necessary conditions for a 15% to 20% reduction in overall platoon wind resistance.

[0078] In some embodiments, such as Figure 4 As shown, in step S2, based on the optimization priority, the chassis attitude parameters and aerodynamic component parameters are solved collaboratively as the quantities to be optimized to obtain the target values ​​of each quantity to be optimized, including: S201. Assign weight coefficients and constraint thresholds to each optimization objective according to the optimization priority. Among them, the safety objective and the heat dissipation objective are entered into the collaborative solution in the form of constraint thresholds. Assign weight coefficients to the other optimization objectives, and the higher the priority of the optimization objective, the larger the corresponding weight coefficient. The sum of all weight coefficients is 1.

[0079] Weighting coefficients are dimensionless real numbers assigned to each optimization objective, representing the urgency of satisfying that objective. Constraint thresholds are the right-hand side of the inequality for each constraint, used to define the legal range of values ​​for the quantity to be optimized. These two are configured according to optimization priority, converting qualitative order into quantitative parameters usable for numerical solution. Higher-priority optimization objectives receive larger weighting coefficients and more lenient constraint margins. The weighting coefficients for each optimization objective under each optimization priority mode are shown in Table 1 below. These are the weighting coefficients for the wind resistance optimization target, the wind noise optimization target, and the energy efficiency benefit item, respectively. The value is determined according to the current optimization priority mode as shown in Table 1; among which, the manual driving mode corresponds to the wind resistance optimization priority mode, the autonomous driving single vehicle driving scenario corresponds to the wind noise optimization priority mode, and the autonomous driving platoon driving scenario corresponds to the energy efficiency optimization priority mode.

[0080] Table 1 S202. Using the minimum weighted sum of the objective functions of each optimization objective as the optimization criterion, solve within the feasible region that satisfies each constraint threshold to obtain at least two sets of candidate parameter combinations.

[0081] The optimization criterion is expressed as follows: In the formula, The parameter combination consists of various quantities to be optimized. For the optimal parameter combination, For the first The weighting coefficients of each optimization objective. For the first The objective function for the optimization objective. For the first One constraint condition. For the first The constraint threshold corresponding to each constraint condition.

[0082] The objective function refers to expressing the degree of deviation from a certain optimization objective as a continuously differentiable function of the quantity to be optimized: the wind resistance optimization objective is the estimated value of the wind resistance coefficient, the wind noise optimization objective is the predicted value of the wind noise inside the vehicle, the heat dissipation objective is the degree of closeness of the highest temperature of the three-electric system to its safety threshold, and the formation energy efficiency objective is the negative number of the formation energy efficiency score.

[0083] The constraints include following distance not less than safe following distance, vehicle height not less than minimum ground clearance corresponding to current road slope, suspension stiffness not less than minimum roll suppression stiffness corresponding to current lateral acceleration, collision time not less than 3 seconds, drive motor temperature not exceeding its safety threshold, and heat dissipation power not less than 1.2 times the current heat dissipation power.

[0084] Outputting at least two sets of candidate parameter combinations is because multi-objective weighted solutions often have multiple weighted sums and similar local minima near the constraint boundary, and choosing only one will result in the loss of a better solution.

[0085] Among them, the target value of suspension stiffness is determined by co-solving within its adjustable range. Its objective function is the weighted deviation of the vehicle pitch angle and roll angle under the current vehicle speed, lateral acceleration and road surface smoothness. At the same time, it must meet the constraint that it is not less than the minimum roll suppression stiffness corresponding to the current lateral acceleration. The target value of the air intake grille opening is obtained by inverse solving the constraint that the heat dissipation power is not less than 1.2 times the current heat dissipation power. That is, the minimum value is taken among all openings that meet this constraint, so as to minimize the separation loss of front airflow and the frontal resistance.

[0086] S203. Select the best of at least two candidate parameter combinations based on the comprehensive performance score, and determine the value of each quantity to be optimized in the candidate parameter combination with the highest comprehensive performance score as the target value.

[0087] The overall performance score is expressed as follows: In the formula, , , These are the predicted values ​​of drag coefficient, in-vehicle wind noise, and energy consumption obtained by predicting the parameter combination X based on the preset model during the collaborative solution stage. , , These represent the baseline values ​​for drag coefficient, in-vehicle wind noise, and energy consumption under the same operating conditions without collaborative solving. This represents the maximum adjustable level of wind noise inside the vehicle. These are the weighting coefficients for wind resistance optimization target, wind noise optimization target, and energy efficiency benefit item, respectively.

[0088] Specifically, in a scenario where a vehicle is cruising at 120 km / h in autonomous driving mode, the optimization priorities are: safety objective, heat dissipation objective, wind noise optimization objective, and wind resistance optimization objective, in descending order. The safety and heat dissipation objectives are addressed through a collaborative solution using constraint thresholds. The constraints are: drive motor temperature threshold of 105℃, heat dissipation power no less than 1.2 times the current heat generation power, in-vehicle wind noise threshold of 60 dB, and longitudinal impact force threshold of 5 m / s². 3 The wind noise optimization target weight coefficient is 0.7, and the wind resistance optimization target weight coefficient is 0.3. The weight coefficients for the comprehensive performance score are determined according to the wind noise optimization priority mode in Table 1, with α set to 0.2, β set to 0.7, and γ set to 0.1.

[0089] The solver, guided by minimizing the weighted sum of the two parameters, outputs two sets of candidate parameter combinations within the feasible region. The first set includes a vehicle height of 143mm, suspension stiffness of 14N / mm, and underbody protection plate opening of 22°; the second set includes a vehicle height of 137mm, suspension stiffness of 16N / mm, and underbody protection plate opening of 12°. A drag coefficient of 0.23, a cabin wind noise of 58dB, and an energy consumption of 20.2kWh / 100km are used as baselines. The maximum adjustable cabin wind noise level is 5dB.

[0090] The predicted drag coefficient for the first group is 0.215, the predicted wind noise inside the vehicle is 54.5 dB, and the predicted energy consumption is 19.2 kWh / 100 km. Substituting these values ​​into the scoring formula and taking the weights according to the wind noise optimization priority mode in Table 1, we get a drag benefit of 0.065, a wind noise benefit of 0.700, an energy efficiency benefit of 0.050, and a comprehensive performance score of 0.508.

[0091] The second group has a predicted drag coefficient of 0.208, a predicted in-vehicle wind noise of 56.5 dB, and a predicted energy consumption of 19.0 kWh / 100 km. The same calculation method yields a drag benefit of 0.096, a wind noise benefit of 0.300, an energy efficiency benefit of 0.059, and a comprehensive performance score of 0.235.

[0092] Based on this, the decision controller selects the first group of optimal values, setting the target value for vehicle height as 143mm, suspension stiffness as 14N / mm, and underbody protection plate opening as 22°.

[0093] The configuration of weight coefficients and constraint thresholds can be achieved by looking up values ​​in a mapping table indexed by optimization priority, or by calculating online using a monotonically decreasing function with the order of each optimization objective as the independent variable; the solution for minimizing the weighted sum can be achieved by gradient optimization using sequential quadratic programming, or by branch and bound search on a discretized parameter grid; the generation of candidate parameter combinations can be achieved by applying different initial values ​​to the solver, or by perturbing the solution neighborhood with a finite step size in the direction of the constraint boundary; the benchmark value in the comprehensive performance score can be obtained by actual measurement when the control flow is closed, or by interpolation from the benchmark curve calibrated by wind tunnel tests.

[0094] This embodiment transforms qualitative priorities into calculable weight coefficients and constraint thresholds, ensuring that optimization priorities are truly translated into solution parameters. Enforcing the sum of weight coefficients to 1 eliminates dimensional drift in the objective function, making the weighted sums of different scenarios horizontally comparable. First, candidate parameter combinations are calculated, then the best is selected based on comprehensive performance scoring, forming a two-level decision-making structure that separates generation from evaluation. This avoids getting trapped in suboptimal local solutions at constraint boundaries and allows the evaluation stage to incorporate actual benefit indicators that are difficult to differentiate during the solution phase. The wind noise benefit term uses difference normalization, making the three benefits additive within the same dimensionless interval.

[0095] In some embodiments, when the quantity to be optimized includes vehicle height, the target value of the vehicle height is determined by the following formula: In the formula, The target value for vehicle body height. Standard vehicle height This represents the maximum vehicle height adjustment range corresponding to the current driving scenario. This is the vehicle speed height adjustment coefficient. This is the wind noise height adjustment coefficient. Current vehicle speed Threshold for vehicle speed activation This represents the current wind noise level inside the vehicle. This is the wind noise compensation threshold.

[0096] Among them, standard vehicle height This is the vehicle's rated ground clearance under normal operating conditions, defaulted to 150mm. Maximum vehicle height adjustment range. This is the upper limit of the vehicle height that can be lowered according to the current driving scenario. For normal scenarios, the default value is 50mm, at which point the vehicle height drops to 100mm, which is exactly the minimum height allowed by the chassis for ground clearance. For cargo-carrying scenarios, 25mm is used to retain the suspension travel required for load-bearing stability. (Vehicle speed height adjustment coefficient) This represents the reduction in vehicle height per unit speed increment, ranging from 0.3 to 0.7, with dimensions in mm / (km / h). The default value is 0.5, determined through wind tunnel testing and fitting. (Wind noise height adjustment coefficient) This is the recovery value for the reduction in vehicle height corresponding to a unit of excessive wind noise, ranging from 1 to 3, with dimensions in mm / dB. The default value is 2, and it is calibrated by fitting from vehicle noise, vibration, and acoustic roughness tests. The default vehicle speed activation threshold is 80 km / h, and the default wind noise compensation threshold is 55 dB.

[0097] Among them, only Not less than If there are no bumpy or uphill sections within the preset distance ahead, the target value for vehicle height will be determined; otherwise, the vehicle height will be maintained at [value missing]. .

[0098] The reason for setting a speed threshold is that when the vehicle speed is below 80km / h, the aerodynamic drag is insufficient, and the benefits gained from lowering the vehicle body cannot compensate for the power consumed by the air compressor and the loss of ride comfort due to frequent suspension adjustments. The reason for setting a road condition threshold is that bumpy roads will cause the suspension to have a large amount of travel. If the vehicle body has been lowered to near the minimum ground clearance, the compression travel will be exhausted, resulting in the limit block impact and possibly causing the underbody protection plate to scrape. On uphill sections, the approach and departure angles are reduced, increasing the risk of bottoming out. These two types of road conditions are pre-identified within a 5km range by the front-mounted LiDAR and high-precision maps. Maintaining the standard vehicle height ensures that the vehicle has recovered to a posture with full suspension travel and passability margin before entering unfavorable road conditions.

[0099] Specifically, a vehicle is cruising at 120 km / h on a highway in normal manual driving mode. The front-mounted lidar and high-precision map confirm that there are no bumpy or uphill sections within 5 km ahead. The vehicle speed is not less than the speed activation threshold of 80 km / h, and the vehicle height target value is activated. At this time, the in-vehicle wind noise level is 58 dB, the speed-to-height adjustment coefficient is 0.5, the wind noise-to-height adjustment coefficient is 2.4, the standard vehicle height is 150 mm, and the maximum vehicle height adjustment is 50 mm. The speed adjustment parameter is 0.5. (120-80)=20mm, wind noise suppression term is 2.4 (58-55)=7.2mm, the intermediate adjustment amount is max[0,20-7.2]=12.8mm, the vehicle height adjustment amount is min(50,12.8)=12.8mm, the target vehicle height value is 150-12.8=137.2mm, and the air spring is deflated accordingly, with an adjustment accuracy of ±2mm. If the vehicle is switched to the autonomous driving comfort mode, the wind noise height adjustment coefficient increases to 3.0 and the vehicle speed height adjustment coefficient decreases to 0.4, then the vehicle speed adjustment item is 16mm, the wind noise suppression item is 9mm, the vehicle height adjustment amount is 7mm, the target vehicle height value is 143mm, and the vehicle height reduction is reduced by about 45% compared to the normal mode. When the vehicle enters a section of road with continuous speed bumps ahead, the road condition threshold is no longer met, and the vehicle height returns to 150mm.

[0100] This embodiment allows the single optimization variable of vehicle height to simultaneously address the opposing demands of wind resistance gains and wind noise costs, achieving an automatic trade-off within a closed expression: the wind noise suppression term intervenes in a subtractive form, ensuring that the vehicle height reduction linearly recovers as excess wind noise exceeds the compensation threshold; the intermediate adjustment is set to a zero lower limit to prevent the vehicle from being raised in the opposite direction; the maximum vehicle height adjustment is related to the driving scenario, allowing the same expression to provide a full 50mm adjustment capability in normal scenarios and shrink to 25mm in cargo scenarios. Real-world testing shows that at 120km / h, the drag coefficient can be reduced by 0.02 and energy consumption by 3.5%, while the underbody scraping damage rate drops from 12% to 3% after automatic disengagement on bumpy and uphill sections.

[0101] In some embodiments, the vehicle speed height adjustment coefficient in the above steps With wind noise height adjustment coefficient All values ​​are determined by multiplying the base value by the scene correction factor, and: In response to the increasing priority of wind noise optimization objectives in the optimization priority list, the [efforts] are increased. Scene correction coefficient; In response to the increasing priority of wind resistance optimization targets or formation energy efficiency targets in the optimization priorities, the following measures are taken: Scene correction coefficient; In response to the tightening of the constraint threshold corresponding to the safety objective in the optimization priority, while reducing and The scene correction coefficient is reduced. .

[0102] Among them, the base value is the original value of the two coefficients obtained directly from the test under the calibration conditions. The base value of the vehicle speed height adjustment coefficient is 0.5, with the dimension of mm / (km / h), and the base value of the wind noise height adjustment coefficient is 2, with the dimension of mm / dB. The scenario correction coefficient is a dimensionless multiplier factor used to shift the base value to the value required for the current scenario. The two are multiplied to obtain the coefficient value actually used in this control cycle, thereby decoupling the physical calibration from the strategy calibration.

[0103] The upward shift in the order of wind noise optimization targets refers to the advancement of the target's position in an ordered sequence, for example, moving it from the last position in manual driving mode to the third position in autonomous driving single-vehicle scenarios. In this case, the scenario correction coefficient for the wind noise height adjustment factor is increased, raising it from the base value of 2 to 2.4 or even 3.0. Since it acts as a subtraction on the intermediate adjustment amount, a larger value means that for the same amount of wind noise excess, more vehicle body reduction is recovered, resulting in a decrease in chassis wind noise. In autonomous driving comfort mode, this scenario correction coefficient is set to 1.5, the wind noise height adjustment factor reaches 3.0, and the vehicle body reduction is reduced by approximately 45% compared to the manual driving normal mode.

[0104] The upward shift in the order of aerodynamic optimization targets or platooning energy efficiency targets refers to the advancement of one of these targets in an ordered sequence. For example, the aerodynamic optimization target is moved forward in manual driving sport mode, or the platooning energy efficiency target is moved forward in autonomous driving platooning scenarios. In this case, the scenario correction coefficient for the vehicle speed-height adjustment factor is increased, raising it from the base value of 0.5 to 0.55 or even 0.6. Since these factors are additive to form the vehicle speed adjustment term, an increased value means a greater reduction in vehicle height at the same speed, resulting in a simultaneous decrease in frontal area and bottom airflow turbulence. In manual driving sport mode, this scenario correction coefficient is set to 1.2, and the vehicle speed-height adjustment factor reaches 0.6; in platooning scenarios, it is set to 1.1, and the vehicle speed-height adjustment factor reaches 0.55.

[0105] Tightening the constraint thresholds corresponding to safety targets means increasing any one of the following: the lower limit of vehicle height, the lower limit of suspension stiffness, or the lower limit of collision time. Typical scenarios include when a vehicle enters a cargo-carrying condition, increasing the suspension travel required for load-bearing stability, or when the road surface enters a low-temperature, wet, and slippery state, causing a decrease in the coefficient of friction. In this case, the scenario correction coefficients of both coefficients are reduced simultaneously, and the maximum adjustment amount of vehicle height is also reduced. The reason for reducing both, rather than just one, is that the vehicle speed adjustment term and the wind noise suppression term have opposite signs in the intermediate adjustment amount: reducing only the former will relatively amplify the wind noise suppression term, reducing the intermediate adjustment amount to zero, while reducing only the latter will actually increase the vehicle height reduction.

[0106] Specifically, in manual driving mode at 120 km / h, the correction coefficients for the two scenarios are 1 and 1.2 respectively. The actual vehicle speed height adjustment coefficient is 0.5, the wind noise height adjustment coefficient is 2.4, the in-vehicle wind noise value is 58 dB, and the vehicle body reduction is 12.8 mm. After switching to sport mode, the wind resistance optimization target order increases, the correction coefficients for the two scenarios become 1.2 and 0.8, and the actual coefficients are 0.6 and 1.6. The vehicle speed adjustment is 24 mm, the wind noise suppression is 4.8 mm, and the vehicle body reduction increases to 19.2 mm, which is about 50% higher than in normal mode. The drag coefficient is further reduced by 0.005, and energy consumption is further reduced by 1%. Subsequently, switching to autonomous driving comfort mode, the correction coefficients for the two scenarios are 0.8 and 1.5, and the actual coefficients are 0.4 and 3.0. The vehicle body reduction shrinks to 7 mm. When the vehicle is loaded with cargo and the road surface is in a low-temperature and slippery condition, the lower limit of the vehicle height is increased, the correction coefficients for the two scenarios are reduced to 0.5 and 1, the maximum adjustment of the vehicle height is reduced from 50mm to 25mm, the vehicle speed adjustment is 10mm, the wind noise suppression is 6mm, the vehicle height is lowered by 4mm, and the vehicle height is maintained at 146mm.

[0107] This embodiment provides a quantitative implementation of the optimization priority order within the vehicle height expression. Each change in priority is reflected in the change of two coefficient values ​​without switching the expression structure. The product form decouples physical calibration from strategy calibration, and strategy iteration only requires modification of the scenario correction coefficient table. The two rules point to two terms with opposite signs in the expression, allowing for a stepless transition between comfort-oriented and economy-oriented vehicle height adjustments. Real-world testing shows that the maximum reduction in vehicle height in cargo scenarios is limited to 25mm, improving passability margin by approximately 30%.

[0108] In some embodiments, such as Figure 5 As shown, after obtaining the vehicle's driving status data and in-vehicle wind noise value, the process includes the following steps S1-1 to S1-4. These four steps are executed between steps S1 and S2, and their numbers are only used to distinguish different steps and do not indicate the order of execution: S1-1. Wind noise signals at different locations are collected by multiple external microphones placed at different positions on the vehicle body.

[0109] The external microphones are microelectromechanical systems (MEMS) microphones installed on the exterior surface of the vehicle body and directly exposed to external airflow. They have a sampling rate of 48kHz, a detection accuracy of ±1dB, and an operating temperature range of -40℃ to 85℃. The four external microphones are located on the left A-pillar, the right A-pillar, the inside of the exterior rearview mirror, and the center of the chassis. These four locations are precisely where airflow separation and vortex shedding are most intense and aerodynamic noise radiation is strongest during high-speed driving.

[0110] Wind noise signal is the time-domain waveform of sound pressure collected by various external microphones at the same time. By collecting wind noise signals at different locations, the total amount measured by the in-vehicle wind noise sensor can be decomposed into components that are distinguished by spatial location, so that the wind noise is transformed from a scalar into a vector that can be located to the specific location of the wind noise.

[0111] S1-2. Convert each wind noise signal into sound power and calculate the proportion of each sound power in the total sound power to obtain the contribution of the wind noise source at each location.

[0112] The collected sound pressure time-domain waveform is subjected to a fast Fourier transform to obtain the spectrum. Then, the sound pressure amplitude is squared within the frequency band of interest and converted according to the air characteristic impedance to obtain the sound power corresponding to the wind noise signal of that channel. The proportion of each channel's sound power in the total sound power is the contribution of the wind noise source corresponding to each location. It is a dimensionless number between 0 and 1. The sum of the four channels is always one, which indicates the proportion of aerodynamic noise at that location in the total wind noise energy of the vehicle.

[0113] S1-3. The wind noise source with the highest contribution and greater than the preset contribution threshold is identified as the target wind noise source.

[0114] The preset contribution threshold is a limit for determining whether a wind noise source is worth being a key target for control, and it is set to 0.3. The highest contribution value is selected to ensure that there is at most one target wind noise source in each control cycle, avoiding multiple wind noise sources from competing for the regulation rights of the same actuator; the additional condition of being greater than the threshold ensures that no target wind noise source is generated when the four contribution values ​​are relatively balanced, and the control process maintains the original weights unchanged.

[0115] S1-4. In collaborative solution, increase the adjustment weight of the optimization quantity corresponding to the target wind noise source in the objective function of the wind noise optimization objective.

[0116] Among them, the quantities to be optimized corresponding to the target wind noise source include at least one of the underbody protection plate opening and the exterior rearview mirror angle.

[0117] The variable to be optimized corresponding to the target wind noise source refers to the adjustable quantity whose adjustment action can directly change the flow field at the target wind noise source: the wind noise source in the middle of the chassis corresponds to the opening of the bottom guard plate, because the opening and closing of the movable cover directly changes the channel cross-section and separation position of the airflow at the bottom, and the sound-absorbing cotton attached to the inside of the cover can absorb the sound energy radiated from the bottom; the wind noise source at the exterior rearview mirror corresponds to the exterior rearview mirror angle, because the attitude of the mirror shell directly determines the intensity and shedding frequency of its wake vortex. Increasing the adjustment weight increases the sensitivity coefficient of the variable to the wind noise optimization objective function, so that the solver prioritizes moving the variable to be optimized during optimization.

[0118] Specifically, in a scenario where a vehicle is cruising at 120 km / h in autonomous driving mode, the wind noise level inside the vehicle is 58 dB. The wind noise optimization target is higher than the wind resistance optimization target. Four external microphones simultaneously collect wind noise signals and convert them into sound power. The contribution of the left A-pillar is 0.18, the right A-pillar is 0.16, the inner side of the exterior rearview mirror is 0.24, and the center of the chassis is 0.42, with a sum of 1. Among them, the contribution of the center of the chassis at 0.42 is both the highest and greater than the preset contribution threshold of 0.3. Based on this, the chassis wind noise source is determined to be the target wind noise source.

[0119] The decision controller then doubled the sensitivity coefficient of the underbody shield opening in the wind noise optimization objective function from the base value. The result of the collaborative solution changed from mainly relying on reducing the vehicle height to suppressing wind noise, to mainly relying on adjusting the underbody shield opening to 22°, which simultaneously achieves the optimal angle for the sound-absorbing cotton exposure area and the separation of airflow at the bottom. After execution, the wind noise value inside the vehicle decreased from 58dB to 54dB, and the sound power proportion of the chassis center dropped back to 0.28. In the next control cycle, the contributions of the four channels were 0.22, 0.20, 0.30, and 0.28, respectively. The target wind noise source shifted to the exterior rearview mirror, and the decision controller then reduced the mirror's outward tilt angle by 2°.

[0120] This embodiment transforms wind noise control from overall passive noise reduction to targeted active suppression: wind noise is decomposed into contribution vectors differentiated by spatial location, enabling the control system to identify noise sources; the proportion is calculated using sound power rather than sound pressure level, correcting the dimensional error of directly comparing quantities; the setting of a single target wind noise source avoids both command conflicts and ineffective adjustments when noise energy is dispersed. Actual measurements show that this method improves noise reduction efficiency by more than 40% compared to uniformly increasing wind noise weight across the entire vehicle, and the weight of chassis sound insulation material is reduced from 12kg to 7.8kg, a reduction of 35%.

[0121] In some embodiments, when the driving scenario is a platooning scenario, the quantity to be optimized also includes the following distance, and the target value of the following distance is not less than the safe following distance; The safe following distance is determined by the following formula: In the formula, The safe following distance is... This is the lower limit of the wake following distance. Current vehicle speed This refers to the total response time from when the vehicle receives the vehicle-to-vehicle cooperative data to when the braking system builds up pressure. This is the maximum braking deceleration of the vehicle. The maximum braking deceleration of the preceding vehicle is obtained through the vehicle-to-vehicle cooperative data. For safety redundancy distance.

[0122] The following distance refers to the longitudinal distance between the front bumper of our vehicle and the rear bumper of the vehicle in front. It is measured by a forward-facing millimeter-wave radar with an accuracy of ±10cm and cross-checked with the positional offset in the vehicle-to-vehicle collaborative data. The following distance is included in the parameters to be optimized because it directly determines the depth of our vehicle within the low-pressure wake zone of the vehicle in front. The smaller the following distance, the lower the oncoming dynamic pressure on our vehicle, and the lower the wind resistance and wind noise.

[0123] Safe following distance It is the minimum longitudinal distance that must be maintained to ensure that the vehicle in front does not rear-end another vehicle when the vehicle in front applies maximum braking force, given a given vehicle speed and the braking capabilities of the vehicles in front and behind. The target value of the specified following distance shall not be less than the safe following distance. That is, maintaining a safe following distance. As the lower bound of the feasible region in the following distance dimension for collaborative solution, the pursuit of aerodynamic benefits is limited to the range allowed by braking safety.

[0124] Specifically, a three-vehicle convoy is traveling at 100 km / h (equivalent to 27.78 m / s) on a highway in autonomous driving mode. This vehicle is the second one, and its maximum braking deceleration is 8 m / s². 2And through vehicle-to-vehicle cooperative data, it was determined that the maximum braking deceleration of the vehicle in front was 7 m / s². 2 The communication response shift is 27.78. 0.3 = 8.33 meters, therefore the complete braking distance of this vehicle is 27.78 meters. 2 / (2 8) = 48.23 meters, the complete braking distance of the vehicle in front is 27.78 meters. 2 / (2 7) = 55.12 meters. The braking distance requirement determined by vehicle-to-vehicle coordinated braking is 8.33 + 48.23 - 55.12 + 5 = 6.44 meters. Taking the larger value between the lower limit of the wake following distance of 15 meters and 6.44 meters, we get 15 meters. If vehicle-to-vehicle communication is interrupted and the braking capability of the vehicle in front cannot be known, it can only be treated as if the vehicle in front is momentarily stationary. The braking distance of the vehicle in front is reset to zero, and the safe following distance becomes 8.33 + 48.23 + 5 = 61.56 meters. This vehicle will not be able to enter the low-pressure wake zone behind the vehicle in front.

[0125] This embodiment establishes a strict quantitative boundary between the aerodynamic benefits and braking safety brought about by close following in a formation: under the condition of 100km / h and vehicle-to-vehicle coordinated braking, the safe following distance is 15 meters, which allows the vehicle to enter the low-pressure wake zone of the vehicle in front, thereby reducing the overall wind resistance of the formation by 15% to 20% and wind noise by 4 to 5dB.

[0126] In some embodiments, such as Figure 6 As shown, when the driving scenario is a platoon driving scenario, collaborative solving also includes: S204. Solve the aerodynamic component parameters of each vehicle in stages according to the order of the vehicles in the formation, so that the underbody opening of the vehicle with the first order is not greater than the underbody opening of the vehicle with the last order.

[0127] The order of vehicles in a platoon refers to the position number obtained by sequentially numbering each vehicle along the direction of travel, starting from the lead vehicle. This number is uniformly assigned by the platoon coordination controller based on the position offset. Hierarchical solution means that the platoon coordination controller incorporates the aerodynamic parameters of all vehicles in the platoon into the same problem and determines them together, assigning different solution objectives to vehicles in different positions according to their order. The physical basis for ensuring that the underbody opening of the leading vehicle is no greater than that of the following vehicles is as follows: the lead vehicle travels directly into the undisturbed oncoming flow, and its wind resistance accounts for the highest proportion of the total wind resistance of the platoon; therefore, its underbody should preferentially close. Following vehicles, however, travel in the low-pressure wake zone behind the leading vehicle, where the oncoming dynamic pressure is significantly reduced. The wind resistance of the first following vehicle can be reduced by 15%, and that of the second following vehicle by 25%. Moderately opening their underbody will not significantly increase the total wind resistance of the platoon; instead, it can improve heat dissipation.

[0128] S205. Calculate the formation energy efficiency score, and when the formation energy efficiency score is lower than the preset score threshold, adjust the initial value of the underbody protection plate opening of each vehicle or relax the upper limit of the underbody protection plate opening of the vehicles with the later order, and re-execute the graded solution until the formation energy efficiency score is not lower than the preset score threshold.

[0129] In the formula, the formation energy efficiency score Average energy consumption percentage Aerodynamic benefits Energy recovery efficiency Synchronization loss The weighted sum, where the values ​​of the above four terms are all limited to between 0 and 1; weight coefficients The sum is 1, and the weights of each item decrease sequentially. The average energy consumption of all vehicles in the platoon. This represents the average energy consumption of a single vehicle under the same operating conditions. The average drag coefficient when traveling in formation. This represents the drag coefficient of a single vehicle. For the total energy recovered during convoy driving, Total energy consumed during platooning; To synchronize acceleration and deceleration during convoy driving, This represents the total power output for convoy driving.

[0130] The formation energy efficiency score is a global benefit score for the entire set of parameters given by the graded solution. The value ranges between zero and one, with a higher value indicating better overall formation energy efficiency. The preset score threshold is the lowest score that the set of parameters is considered acceptable. If the score does not meet the standard, the initial value of the underbody protection plate opening of each vehicle is adjusted or the upper limit of the opening of the following vehicles is relaxed, and the solution is recalculated until the score is not lower than the preset score threshold.

[0131] Specifically, a three-vehicle convoy is traveling at 100 km / h on a highway in autonomous driving mode. The convoy coordination controller determines the order of the lead vehicle as first, the first following vehicle as second, and the second following vehicle as third based on the position offset. In a step-by-step solution for convoy climbing, the output underbody opening is 10° for the lead vehicle, 20° for the first following vehicle, and 30° for the second following vehicle. These three values ​​satisfy the relationship that the opening of the lead vehicle's underbody is no greater than that of the following vehicles. The lead vehicle, with its nearly closed underbody configuration, reduces the overall wind resistance of the convoy. The two following vehicles moderately open their underbody to improve heat dissipation by utilizing the lower oncoming dynamic pressure.

[0132] The formation coordination controller then calculates the formation energy efficiency score, taking the average energy consumption of the formation as 18.5 kWh / 100km, the average energy consumption of non-formation driving as 20.9 kWh / 100km, the average drag coefficient of the formation as 0.195, the drag coefficient of a single vehicle as 0.23, the total energy recovered by the formation as 3.6 kWh, the total energy consumed as 28.5 kWh, the synchronous power loss as 1.2 kW, and the total power of the formation as 95 kW. The four values ​​are 0.115, 0.152, 0.126, and 0.987, respectively. The weighted summation with weights of 0.4, 0.3, 0.2, and 0.1 yields a formation energy efficiency score of 0.216, which is higher than the preset score threshold of 0.200. This set of parameters is accepted and issued for execution.

[0133] If the initial value of the lead vehicle's underbody protection plate opening is set to 30° and all following vehicles have the opening at 30°, although the "not greater than" relationship is still satisfied, the average drag coefficient of the formation increases to 0.208, the aerodynamic benefit decreases to 0.096, and the formation's energy efficiency score drops to 0.199, which is below the preset score threshold. The formation coordination controller then lowers the initial value of the lead vehicle's underbody protection plate opening to 10° and re-executes the hierarchical solution. After implementing this set of parameters, the overall energy consumption of the formation decreases by 11.5%, and the highest temperature of the three-electric system is 98℃, which is within a safe range.

[0134] This embodiment transforms the formation from several independently optimized individuals into a unified, optimized whole: a constraint of no greater than a certain value is imposed on the underbody opening based on the order of the vehicles, ensuring that the lead vehicle handles wind resistance suppression while the following vehicles share the task of heat dissipation. These two mutually constraining requirements are spatially decoupled at the formation scale. Actual measurements show that compared to all vehicles uniformly closing the underbody, overall energy consumption is reduced by 2.1%, and the highest temperature of the three-electric system is only 98℃. The formation energy efficiency score constitutes a two-level structure separating the solution and evaluation. The weights of its four sub-indicators decrease sequentially, making energy consumption the dominant factor in the score result. Values ​​are uniformly truncated between zero and one to ensure comparability across different formation sizes and operating conditions. Actual measurements show that unified formation optimization, compared to independent optimization of each vehicle, further reduces overall wind resistance by 4.2% and overall energy consumption by 2.8%.

[0135] The following are system embodiments of the present invention, which can be used to execute the multi-system collaborative wind resistance and wind noise dynamic optimization method involved in the present invention. For details not disclosed in the system embodiments of the present invention, please refer to the method embodiments of the multi-system collaborative wind resistance and wind noise dynamic optimization method involved in the present invention.

[0136] like Figure 7 As shown in the embodiment of this application, a multi-system collaborative wind resistance and wind noise dynamic optimization system 50 is provided. The system includes: The priority decision module 501 is configured to acquire vehicle driving status data and in-vehicle wind noise value, and determine optimization priority based on the current driving mode and driving scenario. The optimization priority is used to characterize the relative order between the wind resistance optimization target and the wind noise optimization target. The parameter solving module 502 is configured to solve the chassis attitude parameters and aerodynamic component parameters as the quantities to be optimized based on the optimization priority, and obtain the target values ​​of each quantity to be optimized; wherein, the chassis attitude parameters include at least one of vehicle height and suspension stiffness, and the aerodynamic component parameters include at least one of underbody protection plate opening, air intake grille opening and exterior rearview mirror angle. The control and feedback module 503 is configured to control the corresponding actuator action according to the target value, and collect the in-vehicle wind noise value and wind resistance characterization after the actuator action; The dynamic correction module 504 is configured to dynamically correct the target values ​​of each quantity to be optimized based on the collected in-vehicle wind noise value and wind resistance characterization, and to re-determine the optimization priority when the driving mode or driving scenario is switched.

[0137] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules when performing dynamic optimization of wind resistance and wind noise. In actual applications, the above functions can be assigned to different functional modules as needed.

[0138] Furthermore, the above system embodiments and method embodiments belong to the same concept, and the specific way in which each module performs operations has been described in detail in the method embodiments, and will not be repeated here.

[0139] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A multi-system collaborative wind resistance and wind noise dynamic optimization method, characterized in that, The method includes: The vehicle's driving status data and in-vehicle wind noise value are acquired, and the optimization priority is determined based on the current driving mode and driving scenario. The optimization priority is used to characterize the relative order between the wind resistance optimization target and the wind noise optimization target. Based on the optimization priority, chassis attitude parameters and aerodynamic component parameters are used as the optimization quantities to be solved collaboratively to obtain the target values ​​of each of the optimization quantities; wherein, the chassis attitude parameters include at least one of vehicle height and suspension stiffness, and the aerodynamic component parameters include at least one of underbody protection plate opening, air intake grille opening, and exterior rearview mirror angle; Control the corresponding actuator to move according to the target value, and collect the in-vehicle wind noise value and wind resistance value after the actuator moves; The target values ​​of each of the quantities to be optimized are dynamically corrected based on the collected in-vehicle wind noise value and wind resistance value, and the optimization priority is re-determined when the driving mode or the driving scenario is switched.

2. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 1, characterized in that, The process of determining optimization priorities based on the current driving mode and driving scenario includes: Based on the validity of the vehicle-to-vehicle coordination data in the driving status data, the driving scenario is determined to be either a platoon driving scenario or a single-vehicle driving scenario; In response to the driving mode being manual driving mode, the order of the optimization priorities—safety target, heat dissipation target, wind resistance optimization target, and wind noise optimization target—is determined to decrease sequentially. In response to the driving mode being autonomous driving mode and the driving scenario being a single-vehicle driving scenario, the order of the optimization priority targets—safety target, heat dissipation target, wind noise optimization target, and wind resistance optimization target—is determined to decrease sequentially. In response to the driving mode being autonomous driving mode and the driving scenario being platooning driving scenario, the order of the safety target, platooning energy efficiency target and wind noise optimization target in the optimization priority is determined to decrease sequentially, and the platooning energy efficiency target includes the benefit item of the wind resistance optimization target; The driving status data includes environmental perception data, vehicle status data, and vehicle-to-vehicle cooperative data.

3. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 2, characterized in that, Before determining the optimization priority based on the current driving mode and driving scenario, the following is also included: Safety risks are identified based on the driving status data, and the safety risks include at least one of collision risk, water wading risk, and core component overheating risk; In response to the identification of the security risk, a security control strategy corresponding to the identified security risk is executed, and the current collaborative solution is skipped; In response to the failure to identify the aforementioned safety risk, the step of determining the optimization priority based on the current driving mode and driving scenario is executed; The safety constraint threshold satisfied by the collaborative solution changes with the driving scenario: when the driving scenario is a platoon driving scenario, the safety constraint threshold is determined by vehicle-to-vehicle cooperative braking; when the driving scenario is a single-vehicle driving scenario, the safety constraint threshold is determined by single-vehicle braking.

4. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 1, characterized in that, Based on the optimization priority, the chassis attitude parameters and aerodynamic component parameters are used as the quantities to be optimized for collaborative solution to obtain the target values ​​of each of the quantities to be optimized, including: According to the optimization priority, weight coefficients and constraint thresholds are configured for each optimization objective. Among them, the safety objective and the heat dissipation objective are entered into the collaborative solution in the form of the constraint thresholds. The remaining optimization objectives are configured with the weight coefficients, and the higher the order of the optimization objective, the larger the weight coefficients are. The sum of all the weight coefficients is 1. Using the minimum weighted sum of the objective functions of each of the optimization objectives as the optimization criterion, the solution is obtained within the feasible region that satisfies each of the constraint thresholds, resulting in at least two sets of candidate parameter combinations. The optimization criterion is expressed as follows: Based on the comprehensive performance score, the optimal combination of the at least two candidate parameters is selected, and the value of each of the quantities to be optimized in the candidate parameter combination with the highest comprehensive performance score is determined as the target value. The overall performance score is expressed as follows: In the formula, , , The collaborative solution stage involves combining parameters based on a pre-defined model. The predicted values ​​of drag coefficient, in-vehicle wind noise, and energy consumption are obtained. , , These represent the baseline values ​​for drag coefficient, in-vehicle wind noise, and energy consumption under the same operating conditions without collaborative solving. This represents the maximum adjustable level of wind noise inside the vehicle. These are the weighting coefficients for wind resistance optimization target, wind noise optimization target, and energy efficiency benefit item, respectively.

5. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 4, characterized in that, When the quantity to be optimized includes vehicle height, the target value of the vehicle height is determined by the following formula: In the formula, The target value for the vehicle body height. Standard vehicle height This represents the maximum vehicle height adjustment range corresponding to the current driving scenario. This is the vehicle speed height adjustment coefficient. This is the wind noise height adjustment coefficient. Current vehicle speed Threshold for vehicle speed activation This represents the current wind noise level inside the vehicle. This is the wind noise compensation threshold; Among them, only Not less than If there are no bumpy or uphill sections within the preset distance ahead, the target value for the vehicle height is determined; otherwise, the vehicle height is maintained at [value missing]. .

6. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 5, characterized in that, The vehicle speed height adjustment coefficient With the wind noise height adjustment coefficient All values ​​are determined by multiplying the base value by the scene correction factor, and: In response to the increasing order of the wind noise optimization objective in the optimization priority, the [efforts] are increased. Scene correction coefficient; In response to the increasing order of wind resistance optimization target or formation energy efficiency target in the optimization priority, the [efficiency / reduction] is increased. Scene correction coefficient; In response to the tightening of the constraint threshold corresponding to the safety objective in the optimization priority, while reducing and The scene correction coefficient is reduced. .

7. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 1, characterized in that, After acquiring the vehicle's driving status data and in-vehicle wind noise value, the process also includes: Wind noise signals at different locations are collected using multiple external microphones placed at different positions on the vehicle body. The wind noise signals of each channel are converted into sound power, and the proportion of the sound power of each channel in the total sound power is calculated to obtain the contribution of the wind noise source at each location. The wind noise source with the highest contribution and greater than the preset contribution threshold is identified as the target wind noise source; In the collaborative solution, the adjustment weight of the optimization quantity corresponding to the target wind noise source in the objective function of the wind noise optimization objective is increased; wherein, the optimization quantity corresponding to the target wind noise source includes at least one of the underbody panel opening and the exterior rearview mirror angle.

8. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 2, characterized in that, When the driving scenario is a platoon driving scenario, the quantity to be optimized also includes the following distance, and the target value of the following distance is not less than the safe following distance; The safe following distance is determined by the following formula: In the formula, The safe following distance is... This is the lower limit of the wake following distance. Current vehicle speed This refers to the total response time from when the vehicle receives the vehicle-to-vehicle cooperative data to when the braking system builds up pressure. This is the maximum braking deceleration of the vehicle. The maximum braking deceleration of the preceding vehicle is obtained through the vehicle-to-vehicle cooperative data. For safety redundancy distance.

9. The multi-system collaborative dynamic optimization method for wind resistance and noise according to claim 8, characterized in that, When the driving scenario is a platoon driving scenario, the collaborative solution also includes: The pneumatic component parameters of each vehicle are solved in stages according to the order of the vehicles in the formation, so that the underbody opening of the vehicle with the first order is not greater than the underbody opening of the vehicle with the last order. Calculate the formation energy efficiency score, and when the formation energy efficiency score is lower than the preset score threshold, adjust the initial value of the underbody protection plate opening of each vehicle or relax the upper limit of the underbody protection plate opening of the vehicle with the later order, and re-execute the graded solution until the formation energy efficiency score is not lower than the preset score threshold. The formation energy efficiency score is a weighted sum of average energy consumption ratio, aerodynamic benefits, energy recovery efficiency and collaborative synchronization loss. The values ​​of each item are limited to between 0 and 1, and the weights of each item decrease sequentially.

10. A multi-system collaborative dynamic optimization system for wind resistance and noise, characterized in that, The system includes: The priority decision module is configured to acquire vehicle driving status data and in-vehicle wind noise value, and determine optimization priority based on the current driving mode and driving scenario. The optimization priority is used to characterize the relative order between the wind resistance optimization target and the wind noise optimization target. The parameter solving module is configured to perform collaborative solving based on the optimization priority, using chassis attitude parameters and aerodynamic component parameters as the quantities to be optimized, to obtain the target values ​​of each of the quantities to be optimized; wherein, the chassis attitude parameters include at least one of vehicle height and suspension stiffness, and the aerodynamic component parameters include at least one of underbody protection plate opening, air intake grille opening, and exterior rearview mirror angle; The control and feedback module is configured to control the corresponding actuator action according to the target value, and to collect the in-vehicle wind noise value and wind resistance value after the actuator action; The dynamic correction module is configured to dynamically correct the target values ​​of each of the quantities to be optimized based on the collected in-vehicle wind noise value and the wind resistance characterization value, and to re-determine the optimization priority when the driving mode or the driving scenario is switched.

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