Active suspension system control method and device, vehicle and medium

Abstract

The invention relates to the technical field of vehicle active suspension control, in particular to an active suspension system control method and device, a vehicle and a medium, and the method comprises the steps: obtaining elevation information of a front road surface, vehicle body motion state information, local state information of an active suspension and wheels, and driver operation information from a vehicle bus driver; generating fusion information according to the information, and predicting a vehicle state change trend and road excitation characteristics according to the fusion information; according to the vehicle state change trend, the road surface excitation characteristics and the driver operation signal, determining a weight coefficient in a next control period, and generating a target control force sequence; and controlling the active suspension actuator according to the target control force sequence. Therefore, the problems of delay, multi-target conflict and the like existing in an active suspension control method in the related technology are solved.

Description

Technical Field

[0001] This application relates to the field of active suspension control technology for vehicles, and in particular to an active suspension system control method, device, vehicle, and medium. Background Technology

[0002] In the field of active suspension control, active suspension is a key component for improving vehicle ride comfort and stability. However, most active suspension control methods in related technologies are passive response control or simple anticipation control, which have inherent control delays and do not take into account the real-time dynamics of the vehicle. This leads to prominent multi-objective conflicts between ride comfort and stability under different operating conditions, making it difficult to meet the high-performance driving requirements of vehicles. Summary of the Invention

[0003] This application provides an active suspension system control method, device, vehicle, and medium to solve problems such as delay and multi-objective conflict in related active suspension control methods.

[0004] The first aspect of this application provides an active suspension system control method, comprising the following steps: acquiring forward road elevation information, vehicle motion state information, local state information of the active suspension system and wheels, and driver operation information from a vehicle bus driver; generating fused information based on the forward road elevation information, vehicle motion state information, local state information, and driver operation information; predicting vehicle state change trends and road excitation characteristics based on the fused information; determining weighting coefficients for the next control cycle based on the vehicle state change trends, road excitation characteristics, and driver operation signals; generating a target control force sequence based on the vehicle state change trends, road excitation characteristics, and weighting coefficients; and controlling the active suspension actuators of the active suspension system based on the target control force sequence.

[0005] Based on the aforementioned technical means, this embodiment integrates information such as the elevation of the road surface ahead, the vehicle's motion state, the local state of the suspension and wheels, and driver operation information to achieve accurate prediction of vehicle state change trends and road excitation characteristics. Based on the prediction results, it dynamically adjusts control weights and generates a target control force sequence, ultimately driving the active suspension actuators to perform control. By leveraging the anticipation prediction mechanism, it effectively overcomes the control delay bottleneck of the passive response of the active suspension. Through a condition-adaptive weight adjustment strategy, it resolves the multi-objective conflict between ride comfort and stability under different driving conditions. Relying on full-link closed-loop control logic, it significantly improves the control accuracy and condition adaptability of the active suspension.

[0006] Optionally, fused information is generated based on the road elevation information ahead, vehicle motion status information, local status information, and driver operation information, including: synchronizing the road elevation information ahead, vehicle motion status information, local status information, and driver operation signals in time, and performing data fusion processing based on the time-synchronized information to obtain fused information.

[0007] Based on the aforementioned technical means, this embodiment of the application synchronizes the road surface elevation information, vehicle motion state information, local state information, and driver operation signals in time, and then performs data fusion processing on the synchronized multi-source information to generate accurate and consistent fused information. This effectively eliminates the time sequence deviation of information between different sensors and different acquisition modules, avoids prediction errors caused by data asynchrony, and provides reliable data support for the accurate prediction of subsequent vehicle state change trends and road excitation characteristics.

[0008] Optionally, predicting vehicle state change trends and road surface excitation characteristics based on fused information includes: invoking a vehicle dynamics model and a path prediction algorithm; predicting vehicle state change trends based on fused information and the vehicle dynamics model; and predicting road surface excitation characteristics based on fused information and the path prediction algorithm.

[0009] Based on the aforementioned technical means, this application embodiment uses a vehicle dynamics model and a path prediction algorithm to predict vehicle state change trends and road excitation characteristics by combining fused information. Relying on the accurate extrapolation capabilities of the dynamics model, reliable predictions of vehicle state changes such as vehicle posture and tire load are achieved; by leveraging the path prediction algorithm's ability to analyze road information, the specific characteristics of future road excitations are clarified, providing a scientific and accurate decision-making basis for the subsequent dynamic adjustment of control weights and the generation of the target control force sequence.

[0010] Optionally, the weighting coefficients for the next control cycle are determined based on the vehicle state change trend, road excitation characteristics, and driver operation signals, including: determining the vehicle operating condition based on the vehicle state change trend; determining the road impact intensity based on the road excitation characteristics; determining the vehicle target operation based on the driver operation signals; determining the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on the vehicle operating condition and road impact intensity; and determining the weight of at least one of the vehicle roll target and vehicle pitch target based on the vehicle target operation.

[0011] Based on the aforementioned technical means, this application embodiment determines the vehicle's operating condition by analyzing the trend of vehicle state changes, determines the road impact intensity by combining road excitation characteristics, and determines the vehicle's target operation based on the driver's operation signals. Then, it specifically allocates weight coefficients for targets such as ride comfort, suspension travel protection, energy consumption, and vehicle roll and pitch. This achieves condition-adaptive allocation of control target weights, avoiding the defects of fixed weights and poor adaptability in related technologies. It ensures that the weight coefficients accurately match the vehicle's real-time driving state and road conditions, laying the decision-making foundation for generating the optimal target control force sequence.

[0012] Optionally, the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target is determined based on the vehicle operating condition and road impact intensity, including: increasing the respective weights of the ride comfort target and suspension travel protection target if the road impact intensity is greater than the predicted intensity; increasing the respective weights of the ride comfort target and energy consumption target if the vehicle operating condition is a preset operating condition; and reading decision logic from at least one of the rule base and intelligent decision module.

[0013] Based on the aforementioned technical means, this application embodiment dynamically adjusts the weights of ride comfort, suspension travel protection, and energy consumption targets based on the determination results of road impact intensity and vehicle operating conditions. The decision logic is determined according to either the rule base or the intelligent decision module, or jointly by the rule base and the intelligent decision module. This achieves refined and intelligent weight allocation, prioritizing ride comfort and suspension structural safety for high-impact road surfaces, and balancing ride comfort and energy consumption optimization for preset operating conditions. It avoids the limitations of a single weight strategy and provides a flexible and reliable execution basis for multi-objective collaborative control.

[0014] Optionally, the weight of at least one of the vehicle roll target and the vehicle pitch target is determined based on the vehicle target operation, including: increasing the weight of the vehicle roll suppression target if the vehicle target operation is an emergency steering operation; and increasing the weight of the vehicle pitch suppression target if the vehicle target operation is an emergency braking operation.

[0015] Based on the aforementioned technical means, the embodiments of this application, based on the driver's emergency steering, emergency braking, and other target operations, specifically increase the weight of suppressing vehicle roll or vehicle pitch. This achieves a precise match between the control target and the driver's operational intent, prioritizing vehicle handling stability under extreme operating conditions. It effectively solves the problem in related technologies where control methods do not incorporate driver operational intent, easily leading to conflicts between smoothness and stability, and improves driving safety under complex operating conditions.

[0016] Optionally, the target control force sequence is generated based on the vehicle state change trend, road excitation characteristics, and weighting coefficients, including: obtaining the constraints of the active suspension actuators and suspension travel; constructing a control optimization problem based on the vehicle state change trend, road excitation characteristics, weighting coefficients, constraints, and multi-objective functions; and determining the target control force sequence based on the solution results of the control optimization problem.

[0017] Based on the aforementioned technical means, this embodiment of the application obtains the constraints of the active suspension actuator and suspension travel, and constructs a control optimization problem by combining the vehicle state change trend, road excitation characteristics, weighting coefficients, and multi-objective functions. Finally, the target control force sequence is determined based on the solution to the optimization problem. By combining hardware constraints with multi-objective control requirements, it ensures that the generated control force sequence not only meets the upper limit of the suspension actuator's execution capability but also satisfies the control objective requirements under different operating conditions. This avoids control failure caused by exceeding hardware limits and improves the feasibility and effectiveness of the control strategy.

[0018] A second aspect of this application provides an active suspension system control device, comprising: an acquisition module for acquiring forward road elevation information, vehicle motion state information, local state information of the active suspension system and wheels, and driver operation information from a vehicle bus driver; a prediction module for generating fused information based on the forward road elevation information, vehicle motion state information, local state information, and driver operation information, and predicting vehicle state change trends and road excitation characteristics based on the fused information; a generation module for determining weighting coefficients for the next control cycle based on the vehicle state change trend, road excitation characteristics, and driver operation signals, and generating a target control force sequence based on the vehicle state change trend, road excitation characteristics, and weighting coefficients; and a control module for controlling the active suspension actuators of the active suspension system according to the target control force sequence.

[0019] Optionally, the prediction module is further used to: generate fused information based on the road elevation information ahead, vehicle motion status information, local status information and driver operation information, including: synchronizing the road elevation information ahead, vehicle motion status information, local status information and driver operation signals in time, and performing data fusion processing based on the time-synchronized information to obtain fused information.

[0020] Optionally, the prediction module is further configured to: predict vehicle state change trends and road surface excitation characteristics based on the fused information, including: invoking the vehicle dynamics model and path prediction algorithm; predicting vehicle state change trends based on the fused information and the vehicle dynamics model; and predicting road surface excitation characteristics based on the fused information and the path prediction algorithm.

[0021] Optionally, the generation module is further configured to: determine the weighting coefficients for the next control cycle based on the vehicle state change trend, road excitation characteristics, and driver operation signals, including: determining the vehicle operating condition based on the vehicle state change trend; determining the road impact intensity based on the road excitation characteristics; determining the vehicle target operation based on the driver operation signals; determining the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on the vehicle operating condition and road impact intensity; and determining the weight of at least one of the vehicle roll target and vehicle pitch target based on the vehicle target operation.

[0022] Optionally, the generation module is further configured to: determine the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on vehicle operating conditions and road impact intensity, including: increasing the respective weights of the ride comfort target and suspension travel protection target if the road impact intensity is greater than the predicted intensity; increasing the respective weights of the ride comfort target and energy consumption target if the vehicle operating conditions are preset operating conditions; and reading decision logic from at least one of the rule base and intelligent decision module.

[0023] Optionally, the generation module is further configured to: determine the weight of at least one of the vehicle roll target and the vehicle pitch target based on the vehicle target operation, including: increasing the weight of the vehicle roll suppression target if the vehicle target operation is an emergency steering operation; and increasing the weight of the vehicle pitch suppression target if the vehicle target operation is an emergency braking operation.

[0024] Optionally, the generation module is further used to: generate a target control force sequence based on the vehicle state change trend, road excitation characteristics, and weighting coefficients, including: obtaining the constraints of the active suspension actuators and suspension travel; constructing a control optimization problem based on the vehicle state change trend, road excitation characteristics, weighting coefficients, constraints, and multi-objective functions; and determining the target control force sequence based on the solution results of the control optimization problem.

[0025] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the active suspension system control method as described above.

[0026] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the active suspension system control method as described in the above embodiments.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an active suspension system control method provided according to an embodiment of this application; Figure 2 This is a flowchart of an active suspension system control method according to an embodiment of this application; Figure 3 This is a block diagram of an active suspension system control device according to an embodiment of this application; Figure 4 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0030] Active suspension is a component that improves vehicle ride comfort and handling stability; its control performance directly affects the vehicle's ride quality and safety. However, existing active suspension control methods have shortcomings and struggle to meet the high-performance requirements of complex driving scenarios. Specifically: Most of the control methods in related technologies are passive response control. The control logic usually needs to start the corresponding control action only after the road impact has been transmitted to the vehicle body. This results in an inherent delay in the control process that is difficult to avoid, making it impossible to respond to road impacts in advance and affecting the control effect.

[0031] The anti-aiming control scheme only uses road surface information from a single point in front of the vehicle. It is difficult to accurately adapt to road surface excitations that are not synchronized between the left and right wheels (such as a potholed road section on one side) or complex and continuous road surface excitations (such as a washboard road), resulting in poor processing performance. At the same time, its control objective is relatively simple and does not fully take into account the real-time dynamic state of the vehicle, such as the changes in body posture under different driving conditions such as turning, acceleration, and braking. This makes it easy for the vehicle to have a conflict between driving smoothness and handling stability under extreme conditions.

[0032] Furthermore, the active suspension system does not make sufficient use of the anti-sighting information, treating it only as a simple feedforward signal for basic pre-adjustment of the suspension's damping or stiffness. It fails to deeply integrate the anti-sighting information with the vehicle's real-time dynamic state to achieve global optimization control, further limiting the improvement of active suspension performance.

[0033] The active suspension system control method, apparatus, vehicle, and medium of this application are described below with reference to the accompanying drawings. Addressing the issues of delay and multi-objective conflict in the active suspension control methods mentioned in the background section, this application provides an active suspension system control method. This method acquires three-dimensional information of the road ahead, real-time vehicle status, and motion parameters through multi-sensor fusion. Combined with a seven-degree-of-freedom dynamic model of the vehicle, it predicts the wheel-road elevation input sequence and the changing trends of vehicle posture and tire dynamic load in the near future. Furthermore, it switches control weights according to different driving conditions, achieving advance prediction, coordinated control, and adaptive optimization of the active suspension. This solves the problems of passive response delay, insufficient utilization of pre-aiming information, and conflicts between ride comfort and stability under multiple conditions in the active suspension control methods of the related art.

[0034] Specifically, Figure 1 This is a flowchart of an active suspension system control method provided in an embodiment of this application.

[0035] like Figure 1 As shown, the active suspension system control method includes the following steps: In step S101, the following information is obtained: the elevation of the road surface ahead, the vehicle motion status information, the local status information of the active suspension system and the wheels, and the driver operation information from the vehicle bus driver.

[0036] It is understood that this application embodiment achieves comprehensive coverage of the data required for active suspension control by simultaneously collecting information on the road surface elevation, vehicle motion status, local status information of the active suspension system and wheels, and driver operation information. These four types of information correspond to the external road environment, the vehicle's own operating conditions, and the driver's operating intentions, respectively. This breaks through the limitations of single information sources in related technologies, not only eliminating the problem of information silos but also providing comprehensive and reliable data support for subsequent information fusion, trend prediction, and weighted decision-making. This is a key prerequisite for realizing active suspension control from passive response to active predictive control.

[0037] Specifically, the elevation information of the road ahead is acquired by the vehicle's forward-looking vision sensor and radar in collaboration, outputting a three-dimensional map of the road ahead. Unlike single-point elevation information, this map can fully reflect the terrain features such as road undulations, potholes, and speed bumps. The vehicle's motion status information is collected by the vehicle's IMU (Inertial Measurement Unit), specifically including the vehicle's longitudinal, lateral, and vertical accelerations, as well as pitch angle, pitch rate, roll angle, and roll rate. The local status information of the active suspension system and wheels is acquired through wheel speed sensors and suspension travel sensors, which can reflect the relative motion status of the four wheels. The driver's operation information comes from the vehicle bus driver, covering key operating parameters such as steering wheel angle, throttle opening, brake pressure, and vehicle speed.

[0038] In step S102, fused information is generated based on the road surface elevation information ahead, vehicle motion state information, local state information and driver operation information, and the vehicle state change trend and road excitation characteristics are predicted based on the fused information.

[0039] It is understood that this application embodiment achieves in-depth data mining and accurate prediction by fusing information on the road surface elevation, vehicle motion state, suspension and wheel local states, and driver operation, and predicting vehicle state change trends and road excitation characteristics based on the fused information. This effectively eliminates temporal deviations and data redundancy from different information sources, transforming scattered environmental, vehicle, and operational information into relevant decision-making basis. This provides a reference for the dynamic adjustment of subsequent control weights and lays the foundation for active suspension to formulate control strategies in advance and avoid the control delays of passive responses in related technologies.

[0040] Specifically, the information fusion processing employs the extended Kalman filter algorithm. First, it defines state vectors containing the vehicle's vertical, pitch, and roll displacements and velocities, as well as the vertical displacements and velocities of the four wheels. Then, it completes optimal state estimation through a two-step "prediction-update" process. Predicting vehicle state change trends involves inputting the fused information into the vehicle dynamics model to extrapolate changes in vehicle attitude and tire dynamic load over a future period. Predicting road surface excitation features involves first obtaining the vehicle's expected future position based on fused information such as vehicle speed and steering wheel angle using a path prediction algorithm. Then, it matches this with a 3D map of the road ahead to generate road surface elevation input sequences for each of the four wheels, and extracts qualitative features such as "high-intensity single impact" and "low-frequency continuous undulations" from these sequences.

[0041] In this embodiment of the application, fused information is generated based on the road elevation information ahead, vehicle motion status information, local status information and driver operation information. This includes: synchronizing the road elevation information ahead, vehicle motion status information, local status information and driver operation signals in time, and performing data fusion processing based on the time-synchronized information to obtain fused information.

[0042] It is understood that the embodiments of this application synchronize the road surface elevation information, vehicle motion state information, local state information, and driver operation signals in time, and then perform data fusion processing on the synchronized multi-source information to generate accurate and consistent fused information. This effectively eliminates the time sequence deviation of information between different sensors and different acquisition modules, avoids prediction errors caused by data asynchrony, and provides reliable data support for the accurate prediction of subsequent vehicle state change trends and road excitation characteristics.

[0043] Specifically, time synchronization unifies multi-source data from forward vision sensors, radar, IMU, wheel speed sensors, suspension travel sensors, and vehicle bus drivers to the same time base, eliminating timing deviations between different acquisition modules. Data fusion processing uses the extended Kalman filter algorithm. The first step is to calculate the predicted state value for the current period based on the optimal state estimate and vehicle dynamics model of the previous period. The second step compares the measured value and the predicted value of the current period, calculates the Kalman gain to make optimal corrections to the predicted value, and finally outputs a more accurate fused optimal vehicle state estimate.

[0044] In this embodiment of the application, predicting vehicle state change trends and road surface excitation characteristics based on fused information includes: invoking a vehicle dynamics model and a path prediction algorithm; predicting vehicle state change trends based on fused information and the vehicle dynamics model; and predicting road surface excitation characteristics based on fused information and the path prediction algorithm.

[0045] It is understood that the embodiments of this application, by invoking vehicle dynamics models and path prediction algorithms, combine fused information to predict vehicle state change trends and road excitation characteristics. Relying on the accurate extrapolation capabilities of the dynamics model, reliable predictions of vehicle state changes such as vehicle attitude and tire load are achieved; and by leveraging the path prediction algorithm's ability to analyze road information, the specific characteristics of future road excitations are clarified, providing a scientific and accurate decision-making basis for the subsequent dynamic adjustment of control weights and the generation of the target control force sequence.

[0046] Specifically, the vehicle dynamics model is a simplified seven-DOF model of the whole vehicle. This model divides the vehicle into sprung mass (body) and unsprung mass (four wheels). Differential equations describe the vertical, pitch, and roll motions of the body and the vertical motion of the four wheels. The equations consider suspension forces (spring force, damping force, active control force) and tire forces. The model parameters include body mass, moment of inertia, wheel mass, suspension stiffness and damping, tire stiffness, etc. After discretization, they are transformed into a state-space form for prediction. The path prediction algorithm is a simplified bicycle model. Based on the current vehicle speed and steering wheel angle, it calculates the yaw angle and lateral displacement changes of the vehicle in the near future, iteratively generates the expected position of the vehicle, and then combines the road elevation information ahead to obtain the future road surface elevation input sequence of the four wheels, thereby extracting road surface excitation features.

[0047] In step S103, the weighting coefficients for the next control cycle are determined based on the vehicle state change trend, road surface excitation characteristics, and driver operation signals. The target control force sequence is then generated based on the vehicle state change trend, road surface excitation characteristics, and weighting coefficients.

[0048] It is understood that the embodiments of this application dynamically determine the control weight coefficients based on the vehicle state change trend, road excitation characteristics, and driver operation signals, and generate a target control force sequence in combination with the above parameters, thereby realizing the condition-adaptive optimization of the control strategy. This overcomes the limitations of fixed weights in related technologies, enabling the control target priority to accurately match the vehicle's real-time driving state and road conditions. This ensures a balance between smoothness, stability, and other objectives under different operating conditions, and by generating a targeted control force sequence, provides instruction support for the precise execution of the active suspension actuators.

[0049] Specifically, the determination of weighting coefficients is performed by the upper-level intelligent decision-making module. This module takes the predicted vehicle state change trend (including the change law of parameters such as displacement, speed, and acceleration), road excitation characteristics (such as impact intensity and road surface type), and driver operation signals as inputs. Based on the built-in rule base or fuzzy inference mechanism, it dynamically adjusts the priority weights of multiple objectives such as ride comfort, handling stability, suspension travel protection, tire contact with the ground, and control energy consumption. The generation of the target control force sequence is performed by the lower-level model predictive controller receiving the weighting coefficients, combining the predicted vehicle state change trend and road excitation sequence, constructing and solving a multi-objective optimization problem, and outputting the optimal actuator force sequence in the future time domain.

[0050] In this embodiment of the application, the weighting coefficients for the next control cycle are determined based on the vehicle state change trend, road excitation characteristics, and driver operation signals, including: determining the vehicle operating condition based on the vehicle state change trend; determining the road impact intensity based on the road excitation characteristics; determining the vehicle target operation based on the driver operation signals; determining the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on the vehicle operating condition and road impact intensity; and determining the weight of at least one of the vehicle body roll target and vehicle body pitch target based on the vehicle target operation.

[0051] It is understood that the embodiments of this application determine the vehicle operating condition by the trend of vehicle state changes, determine the road impact intensity by combining road excitation characteristics, determine the vehicle target operation based on the driver's operation signal, and then allocate weight coefficients for targets such as ride comfort, suspension travel protection, energy consumption, and vehicle roll and pitch. This achieves condition-adaptive allocation of control target weights, avoiding the defects of fixed weights and poor adaptability in related technologies, and enabling the weight coefficients to accurately match the real-time driving state of the vehicle and road conditions, laying the decision-making foundation for generating the optimal target control force sequence.

[0052] It should be noted that road surface excitation features are qualitative descriptions abstracted from the road surface elevation input sequence, used for upper-level decision-making logic, rather than specific road surface height values. Common features include high-intensity single impacts, such as potholes and speed bumps, and low-frequency continuous undulations, such as long-wavelength undulating roads, which directly determine the priority adjustment direction of suspension control objectives. Weighting coefficients are quantified priority values ​​assigned to each suspension control objective, used to adjust the importance of different performance indicators in the multi-objective optimization function. The higher the weighting coefficient, the higher the priority the corresponding control objective is guaranteed during the optimization process, and this coefficient is dynamically adjusted according to operating conditions, rather than remaining fixed. The control cycle is the time unit for the active suspension control system to complete one cycle of "information acquisition, decision-making, control, and feedback," and is the basis for achieving rolling optimization.

[0053] In the next control cycle, the active suspension control system will recalculate the weighting coefficients and control force sequence based on new sensor data to ensure the real-time performance of the control strategy. Road impact intensity is a road input level categorized based on road excitation characteristics, used to measure the impact of road undulations on the vehicle. For example, "high impact intensity" corresponds to potholes, speed bumps, etc., while "low impact intensity" corresponds to smooth roads. The ride comfort target is to minimize the vehicle's vertical acceleration, which physically means reducing the impact and vibration felt by occupants in the vertical direction, improving ride comfort. The suspension travel protection target is to limit the compression and extension of the suspension, keeping it within safe mechanical limits to prevent collision damage to suspension components due to excessive movement. This is typically achieved by tightening constraint boundaries during optimization. The energy consumption target is to limit the actuator output, which physically means reducing the energy consumption of the active suspension actuators and improving system energy efficiency. The body roll target is to suppress body roll, mainly for emergency steering conditions, ensuring vehicle handling stability and preventing excessive body roll from affecting driving safety. The goal of vehicle pitch is to suppress vehicle pitch, mainly for emergency braking conditions, to alleviate the "nose-diving" phenomenon of the vehicle, and to improve the vehicle's attitude stability during braking.

[0054] Specifically, determining vehicle operating conditions based on changes in vehicle status involves analyzing the first and second derivatives (angular velocity and angular acceleration) of the vehicle's attitude parameters. For example, when a sharp increase in the vehicle's pitch angle acceleration is predicted, it is determined to be an operating condition indicating an impending violent "nodding" motion. Determining road impact intensity based on road excitation characteristics involves classifying impact levels based on extracted qualitative features, such as classifying "high-intensity single impact" as a high-impact-intensity operating condition. Determining vehicle target operations based on driver operation signals involves identifying operation types such as emergency steering, emergency braking, and smooth cruise control through parameters such as steering wheel angle and braking pressure. On this basis, for different combinations of operating conditions, the weights of targets such as ride comfort, suspension travel protection, energy consumption, roll suppression, and pitch suppression are differentiated.

[0055] In this embodiment of the application, determining the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on vehicle operating conditions and road impact intensity includes: increasing the respective weights of the ride comfort target and suspension travel protection target if the road impact intensity is greater than the predicted intensity; increasing the respective weights of the ride comfort target and energy consumption target if the vehicle operating conditions are preset conditions; and reading decision logic from at least one of the rule base and intelligent decision module.

[0056] It is understood that the embodiments of this application dynamically adjust the weights of ride comfort, suspension travel protection, and energy consumption targets based on the determination results of road impact intensity and vehicle operating conditions. The decision logic is determined according to either the rule base or the intelligent decision module, or jointly by the rule base and the intelligent decision module. This achieves refined and intelligent weight allocation, prioritizing ride comfort and suspension structural safety for high-impact road surfaces, and balancing ride comfort and energy consumption optimization for preset operating conditions. This avoids the limitations of a single weight strategy and provides a flexible and reliable execution basis for multi-objective collaborative control.

[0057] It should be noted that the preset operating condition refers to the normal and stable driving state of the vehicle, which meets the requirements of no obvious impact on the road surface, stable vehicle posture, and no sudden acceleration, deceleration / emergency steering / braking operations by the driver. Under this operating condition, the suspension control aims to balance ride comfort and energy consumption.

[0058] Specifically, the determination of road impact intensity exceeding the predicted threshold is based on the impact level classification results in the road excitation characteristics. When a high-impact condition is determined, the weights of the ride comfort target and the suspension travel protection target are increased simultaneously. The ride comfort target is quantified as minimizing the square of the vehicle's vertical acceleration, while the suspension travel protection target is achieved by tightening the constraint boundaries. The preset operating condition specifically refers to the smooth cruise condition, in which the weights of the ride comfort target and the energy consumption target are balanced to take into account both driving comfort and energy-saving requirements. The decision logic is read by retrieving preset operating condition and weight mapping rules from the rule base, or by the intelligent decision module combining real-time status for autonomous reasoning. One of the two methods can be selected or used together to ensure the flexibility and accuracy of weight allocation.

[0059] In this embodiment of the application, determining the weight of at least one of the vehicle roll target and the vehicle pitch target based on the vehicle target operation includes: if the vehicle target operation is an emergency steering operation, then increasing the weight of the vehicle roll suppression target; if the vehicle target operation is an emergency braking operation, then increasing the weight of the vehicle pitch suppression target.

[0060] It is understood that the embodiments of this application, based on the driver's emergency steering, emergency braking, and other target operations, specifically increase the weight of suppressing vehicle roll or vehicle pitch. This achieves a precise match between the control target and the driver's operational intent, prioritizing vehicle handling stability under extreme operating conditions. It effectively solves the problem in related technologies where control methods do not incorporate driver operational intent, easily leading to conflicts between smoothness and stability, and improves driving safety under complex operating conditions.

[0061] Specifically, emergency steering is identified by monitoring the rate of change of steering wheel angle transmitted by the vehicle bus driver. When the rate of change exceeds a preset threshold, it is determined to be an emergency steering condition. At this time, the weight of suppressing body roll is increased to prioritize vehicle handling stability. Emergency braking is identified by monitoring brake pressure parameters. When the brake pressure rises sharply, it is determined to be an emergency braking condition. At this time, the weight of suppressing body pitch is increased to alleviate the "nose-diving" phenomenon. The weight adjustment instructions are output by the upper-level intelligent decision-making module and directly affect the multi-objective optimization function of the lower-level model predictive controller.

[0062] In this embodiment of the application, the generation of a target control force sequence based on the vehicle state change trend, road surface excitation characteristics, and weighting coefficients includes: obtaining the constraints of the active suspension actuator and suspension travel; constructing a control optimization problem based on the vehicle state change trend, road surface excitation characteristics, weighting coefficients, constraints, and a multi-objective function; and determining the target control force sequence based on the solution results of the control optimization problem.

[0063] It is understood that this application embodiment obtains the constraints of the active suspension actuator and suspension travel, combines the vehicle state change trend, road excitation characteristics, weighting coefficients, and multi-objective functions to construct a control optimization problem, and finally determines the target control force sequence based on the solution of the optimization problem. By combining hardware constraints with multi-objective control requirements, it ensures that the generated control force sequence not only meets the upper limit of the suspension actuator's execution capability but also satisfies the control objective requirements under different operating conditions, avoiding control failure caused by exceeding hardware limits and improving the feasibility and effectiveness of the control strategy.

[0064] It should be noted that the multi-objective function refers to a mathematical function constructed with multiple control objectives of the active suspension as the optimization direction. Its optimization objectives cover ride comfort, handling stability, suspension travel protection, tire contact patch, and control energy consumption. Among them, ride comfort minimizes the vertical acceleration of the vehicle body, handling stability minimizes the pitch and roll angles of the vehicle body, suspension travel protection limits the compression and extension of the suspension, tire contact patch minimizes the dynamic fluctuation of the vertical load on the tires, and control energy consumption limits the output of the actuators. This function integrates the various objectives into a unified optimization index in the form of a weighted sum of squares. The weight coefficients of each objective are dynamically allocated by the upper-level intelligent decision-making module according to the real-time operating conditions.

[0065] The constraints are hard constraints for active suspension control, including two types: first, active suspension actuator constraints, namely the upper and lower limits of the actuator's output force, to ensure that the control force does not exceed the physical execution limit of the hardware; second, suspension travel constraints, namely the safe range of suspension compression and extension, to prevent suspension components from being damaged due to excessive movement and impact with mechanical limits. The constraints are directly embedded in the solution process of the control optimization problem to ensure that the generated control force sequence is feasible and safe.

[0066] Specifically, the constraints on the active suspension actuators are the upper and lower limits of the actuator output force, and the constraints on the suspension travel are the safe range of suspension compression and extension; both are hard constraints. The control optimization problem is constructed with actuator output force as the optimization variable, and the performance indicators are the minimization of the weighted sum of squares of ride comfort, handling stability, suspension travel protection, tire contact patch, and control energy consumption, while simultaneously embedding the constraints on actuator and suspension travel. Solving the control optimization problem involves transforming it into a quadratic programming problem with linear constraints, using the effective set method or interior point method in real-time within the vehicle-mounted embedded system, and then converting it into C code using a professional code generation tool, ultimately outputting the optimal actuator control force sequence.

[0067] In step S104, the active suspension actuator of the active suspension system is controlled according to the target control force sequence.

[0068] Understandably, by controlling the active suspension actuators through a target control force sequence, a closed-loop control chain of prediction-decision-execution is completed. This step precisely transmits the optimized control commands generated in the preceding steps to the execution components, ensuring that the suspension's adjustment actions are perfectly matched with the vehicle's real-time operating conditions, road surface stimuli, and the driver's operational intentions. This not only ensures the effective implementation of the control strategy but also realizes the transformation of the active suspension from passive response to active adjustment, ultimately achieving the goal of resolving control delay and multi-objective conflict issues.

[0069] Specifically, controlling the active suspension actuator is to execute the first control force command in the optimal control force sequence, which is required by the "rolling optimization" principle of model predictive control. While outputting the command, the actual response data of the vehicle system is collected in real time, including vehicle attitude, suspension travel, wheel load, etc., and fed back to the information fusion module to update the state estimate of the extended Kalman filter and to perform online correction of the vehicle dynamics model, forming a closed-loop control process of "collection, fusion, prediction, decision-making, control, and feedback" to ensure that the control strategy can continuously adapt to changes in actual operating conditions.

[0070] The active suspension system control method proposed in this application integrates information such as the road surface elevation, vehicle motion state, local suspension and wheel states, and driver operation to accurately predict vehicle state change trends and road excitation characteristics. Based on the prediction results, it dynamically adjusts control weights and generates a target control force sequence, ultimately driving the active suspension actuators to perform control. By employing a pre-aiming prediction mechanism, it effectively overcomes the control delay bottleneck of passive suspension response. Through a condition-adaptive weight adjustment strategy, it resolves the multi-objective conflict between ride comfort and stability under different driving conditions. Relying on full-link closed-loop control logic, it significantly improves the control accuracy and condition adaptability of the active suspension.

[0071] The fully active suspension control method proposed in this application will now be described in detail through a specific embodiment, such as... Figure 2 As shown, the specific steps are as follows: In step one, multi-source information acquisition and fusion: through multiple sources such as forward-looking sensors, vehicle inertial measurement units, vehicle height sensors, and controller area network buses, the vehicle simultaneously acquires road elevation information, vehicle motion status information, active suspension and wheel local status information, and driver operation information, and performs time synchronization and data fusion processing on this information to generate a unified optimal vehicle state estimate.

[0072] In step two, vehicle state and road excitation prediction: Based on the fused vehicle state information, the seven-degree-of-freedom dynamics model of the whole vehicle and the path prediction algorithm are called to predict the trend of vehicle body posture change in the short term, as well as the road excitation sequence and excitation characteristics of each of the four wheels.

[0073] In step three, condition identification and weight decision-making: Based on the predicted vehicle state change trend and road excitation characteristics, combined with the driver's operation signal, condition identification is performed to determine whether the vehicle is currently in a stable driving, high-impact, or extreme operation condition; then, the intelligent decision-making module dynamically outputs the multi-objective optimization weight coefficients for the next control cycle based on the rule base or fuzzy inference mechanism.

[0074] In step four, model predictive control optimization is solved: dynamic weight coefficients from the upper layer are received, and combined with the predicted vehicle state, road excitation, and constraints on actuator and suspension travel, a multi-objective optimization problem is constructed and solved to obtain the optimal active suspension actuator control force sequence in the future time domain.

[0075] In step five, the control force is output to the actuator: the first control force command in the optimal control force sequence is output to the corresponding active suspension actuator to perform the suspension adjustment action.

[0076] In step six, the actual response of the system is collected: the actual response data of the vehicle system is collected in real time and fed back to the multi-source information collection and fusion module to update the state estimation and correct the prediction model, forming closed-loop control.

[0077] In summary, the embodiments of this application have at least the following beneficial effects: (1) By deeply integrating forward-looking road information, real-time vehicle dynamics and driver's operating intentions, the coupled dynamic response of the vehicle and road surface in the future time domain can be accurately predicted, which makes up for the shortcomings of related technologies that only identify single road surface features.

[0078] (2) An upper-level intelligent coordinator is adopted to adjust the multi-objective weights of the lower-level controller online and dynamically according to the predicted working conditions and driver intentions, realizing the leap from "fixed rules" to "scenario adaptation" in the control strategy and solving the target conflict between smoothness and stability.

[0079] (3) A complete closed loop from “prediction, decision-making, optimization to execution” was constructed, and the deviation between the actual response and the predicted value was used to perform online real-time correction of the system state and model parameters, which significantly improved the robustness and long-term control accuracy of the system under parameter changes and uncertain disturbances.

[0080] Next, the active suspension system control device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0081] Figure 3 This is a block diagram of the active suspension system control device according to an embodiment of this application.

[0082] like Figure 3 As shown, the active suspension system control device 30 includes: an acquisition module 301, a prediction module 302, a generation module 303, and a control module 304.

[0083] The acquisition module 301 is used to acquire information on the road surface elevation ahead, vehicle motion state, local state information of the active suspension system and wheels, and driver operation information from the vehicle bus driver; the prediction module 302 is used to generate fused information based on the road surface elevation ahead, vehicle motion state, local state, and driver operation information, and predict the vehicle state change trend and road excitation characteristics based on the fused information; the generation module 303 is used to determine the weight coefficients in the next control cycle based on the vehicle state change trend, road excitation characteristics, and driver operation signals, and generate a target control force sequence based on the vehicle state change trend, road excitation characteristics, and weight coefficients; the control module 304 is used to control the active suspension actuators of the active suspension system according to the target control force sequence.

[0084] In this embodiment of the application, the prediction module 302 is further used to: generate fused information based on the road elevation information ahead, vehicle motion state information, local state information and driver operation information, including: synchronizing the road elevation information ahead, vehicle motion state information, local state information and driver operation signals in time, and performing data fusion processing based on the time-synchronized information to obtain fused information.

[0085] In this embodiment, the prediction module 302 is further configured to: predict the vehicle state change trend and road surface excitation characteristics based on the fused information, including: calling the vehicle dynamics model and the path prediction algorithm; predicting the vehicle state change trend based on the fused information and the vehicle dynamics model; and predicting the road surface excitation characteristics based on the fused information and the path prediction algorithm.

[0086] In this embodiment, the generation module 303 is further configured to: determine the weight coefficients for the next control cycle based on the vehicle state change trend, road surface excitation characteristics, and driver operation signals, including: determining the vehicle operating condition based on the vehicle state change trend; determining the road impact intensity based on the road surface excitation characteristics; determining the vehicle target operation based on the driver operation signals; determining the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on the vehicle operating condition and road surface impact intensity; and determining the weight of at least one of the vehicle body roll target and vehicle body pitch target based on the vehicle target operation.

[0087] In this embodiment of the application, the generation module 303 is further configured to: determine the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on the vehicle operating condition and road impact intensity, including: if the road impact intensity is greater than the predicted intensity, then increase the respective weights of the ride comfort target and the suspension travel protection target; if the vehicle operating condition is a preset operating condition, then increase the respective weights of the ride comfort target and the energy consumption target; and read decision logic from at least one of the rule base and the intelligent decision module.

[0088] In this embodiment of the application, the generation module 303 is further configured to: determine the weight of at least one of the vehicle roll target and the vehicle pitch target based on the vehicle target operation, including: if the vehicle target operation is an emergency steering operation, then increase the weight of the vehicle roll suppression target; if the vehicle target operation is an emergency braking operation, then increase the weight of the vehicle pitch suppression target.

[0089] In this embodiment, the generation module 303 is further configured to: generate a target control force sequence based on the vehicle state change trend, road surface excitation characteristics, and weighting coefficients, including: obtaining the constraints of the active suspension actuator and suspension travel; constructing a control optimization problem based on the vehicle state change trend, road surface excitation characteristics, weighting coefficients, constraints, and a multi-objective function; and determining the target control force sequence based on the solution results of the control optimization problem.

[0090] It should be noted that the foregoing explanation of the active suspension system control method embodiment also applies to the active suspension system control device of this embodiment, and will not be repeated here.

[0091] The active suspension system control device proposed in this application integrates information such as the road surface elevation, vehicle motion state, local suspension and wheel states, and driver operation to accurately predict vehicle state change trends and road excitation characteristics. Based on the prediction results, it dynamically adjusts control weights and generates a target control force sequence, ultimately driving the active suspension actuators to perform control. By employing a pre-aiming prediction mechanism, it effectively overcomes the control delay bottleneck of passive suspension response. Through a condition-adaptive weight adjustment strategy, it resolves the multi-objective conflict between ride comfort and stability under different driving conditions. Relying on full-link closed-loop control logic, it significantly improves the control accuracy and condition adaptability of the active suspension.

[0092] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0093] When the processor 402 executes the program, it implements the active suspension system control method provided in the above embodiments.

[0094] Furthermore, the vehicle also includes: Communication interface 403 is used for communication between memory 401 and processor 402.

[0095] The memory 401 is used to store computer programs that can run on the processor 402.

[0096] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0097] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0098] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0099] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0100] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described active suspension system control method.

[0101] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0102] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0103] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0104] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0105] Those skilled in the art will understand that all or part of the steps of the methods implementing the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0106] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A control method for an active suspension system, characterized in that, Includes the following steps: It acquires information on the road surface elevation ahead, vehicle motion status, local status of the active suspension system and wheels, and driver operation information from the vehicle bus driver. Based on the road surface elevation information ahead, the vehicle motion state information, the local state information, and the driver operation information, fused information is generated, and the vehicle state change trend and road excitation characteristics are predicted based on the fused information; Based on the vehicle state change trend, the road surface excitation characteristics, and the driver operation signal, the weight coefficients for the next control cycle are determined, and a target control force sequence is generated based on the vehicle state change trend, the road surface excitation characteristics, and the weight coefficients. The active suspension actuator of the active suspension system is controlled according to the target control force sequence.

2. The active suspension system control method according to claim 1, characterized in that, The step of generating fused information based on the road surface elevation information ahead, the vehicle motion state information, the local state information, and the driver operation information includes: The road surface elevation information, vehicle motion status information, local status information, and driver operation signals are synchronized in time. Data fusion processing is then performed based on the synchronized information to obtain the fused information.

3. The active suspension system control method according to claim 1, characterized in that, The prediction of vehicle state change trends and road excitation characteristics based on the fused information includes: Call upon the vehicle dynamics model and path prediction algorithm; Predict the trend of vehicle state change based on the fused information and the vehicle dynamics model; The road surface excitation features are predicted based on the fused information and the path prediction algorithm.

4. The active suspension system control method according to claim 1, characterized in that, The step of determining the weighting coefficients for the next control cycle based on the vehicle state change trend, the road surface excitation characteristics, and the driver operation signals includes: The vehicle operating condition is determined based on the described trend of vehicle status changes. The road impact intensity is determined based on the road excitation characteristics. The vehicle target operation is determined based on the driver's operation signal; The weight of at least one of the following targets—ride comfort, suspension travel protection, and energy consumption—is determined based on the vehicle operating conditions and the road impact intensity. The weight of at least one of the following targets—body roll and body pitch—is determined based on the vehicle target operation.

5. The active suspension system control method according to claim 4, characterized in that, The step of determining the weight of at least one of the ride comfort target, suspension travel protection target, and energy consumption target based on the vehicle operating conditions and the road impact intensity includes: If the road impact intensity is greater than the predicted intensity, then the respective weights of the ride comfort target and the suspension travel protection target are increased. If the vehicle operating condition is a preset condition, then the respective weights of the smoothness target and the energy consumption target are increased; Read the decision logic from at least one of the rule base and the intelligent decision module.

6. The active suspension system control method according to claim 4, characterized in that, Determining the weight of at least one of the vehicle roll target and the vehicle pitch target based on the vehicle target operation includes: If the target vehicle operation is an emergency steering operation, then the weight of the target for suppressing vehicle roll is increased; If the target vehicle operation is an emergency braking operation, then the weight of suppressing vehicle pitch targets is increased.

7. The active suspension system control method according to claim 4, characterized in that, The step of generating the target control force sequence based on the vehicle state change trend, the road surface excitation characteristics, and the weighting coefficients includes: Obtain the constraints for the active suspension actuators and suspension travel; A control optimization problem is constructed based on the vehicle state change trend, the road surface excitation characteristics, the weighting coefficients, the constraints, and the multi-objective function. The target control force sequence is determined based on the solution results of the control optimization problem.

8. A control device for an active suspension system, characterized in that, include: The acquisition module is used to acquire information on the road surface elevation ahead, vehicle motion status, local status information of the active suspension system and wheels, and driver operation information from the vehicle bus driver. The prediction module is used to generate fused information based on the road surface elevation information ahead, the vehicle motion state information, the local state information and the driver operation information, and to predict the vehicle state change trend and road excitation characteristics based on the fused information; The generation module is used to determine the weight coefficients in the next control cycle based on the vehicle state change trend, the road surface excitation characteristics, and the driver operation signal, and to generate a target control force sequence based on the vehicle state change trend, the road surface excitation characteristics, and the weight coefficients. The control module is used to control the active suspension actuator of the active suspension system according to the target control force sequence.

9. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the active suspension system control method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the active suspension system control method according to any one of claims 1-7.

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