Multi-point independent control electric control suspension method and system
Through visual sensors and model predictive control technology, the electronic suspension parameters are adjusted in real time, solving the complexity and stability problems of the electronic suspension system in the existing technology, realizing efficient and precise adjustment of the electronic suspension system with multi-point independent control, and improving the comfort and stability of the vehicle.
Patent Information
- Application Number
- CN202511014628.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-23
- Publication Date
- 2025-09-05
AI Technical Summary
The existing multi-point independent control electronic suspension system has complex control algorithms, high costs, and difficult maintenance, and faces challenges in control accuracy and stability under extreme working conditions.
Real-time multi-source road data is obtained through visual sensors, and the local binary pattern and Pareto optimization algorithm are combined to generate the initialization control range value of the vehicle's independent electronic suspension. Steering force feedback data and model predictive control methods are used to adjust the suspension parameters in real time, establish a dynamic electronic suspension control model, and achieve multi-point independent control.
It improves the vehicle's driving performance under different road conditions, enhances the vehicle's comfort, stability and handling, and ensures smooth driving in complex environments.
Smart Images
Figure CN120588698A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of supply chain management, and in particular to a multi-point independent control electronically controlled suspension method and system. Background Art
[0002] A suspension control technology that independently adjusts the pressure and control input of each suspension unit to adapt to different road conditions and vehicle body requirements in real time. The system uses advanced control algorithms (such as PID control and optimization algorithms) to independently control each suspension, achieving coordination between various suspension units to improve vehicle comfort, stability and handling during driving. This method can precisely adjust the dynamic response of the suspension, enabling the vehicle to maintain optimal driving performance in complex road environments. Summary of the Invention
[0003] In order to solve the above technical problems, a multi-point independently controlled electronic suspension method and system are provided. This technical solution solves the above-mentioned multi-point independently controlled electronic suspension method and system. Its control algorithm is complex, requiring high-precision sensors and computing power, which increases the cost and maintenance difficulty of the system; since each suspension unit is independently controlled, the system has high real-time response requirements and may face challenges in control accuracy and stability under extreme working conditions.
[0004] In order to achieve the above objects, the technical solution adopted by the present invention is: A multi-point independent control electronically controlled suspension method, comprising: Based on visual sensors, real-time multi-source road surface data can be obtained to analyze road surface roughness and generate road surface condition indicators for the vehicle's forward direction; Obtain the upper and lower limits of the vehicle's electronically controlled suspension's adjustable range and perform correlation analysis with road condition indicators in the vehicle's forward direction to generate an initialization control range value for the vehicle's independent electronically controlled suspension; Obtaining steering force feedback data from a vehicle driver to correct a road surface condition indicator of the vehicle's forward direction in real time, thereby obtaining a road surface condition indicator that updates the vehicle's forward direction in real time; Using a road condition indicator that is updated in real time in the vehicle's forward direction, the initialization control range value of the vehicle's independent electronically controlled suspension is corrected to obtain a to-be-controlled range value of the vehicle's independent electronically controlled suspension; Obtain the pressure data of each independent electronic suspension of the vehicle and the range value to be adjusted of the independent electronic suspension of the vehicle, establish a dynamic electronic suspension control model, and generate a multi-point independent control electronic suspension system solution.
[0005] Preferably, based on a visual sensor, real-time multi-source road surface data is acquired to perform road surface image preprocessing; Based on the road surface image preprocessing, the local binary pattern is used to select the image region of interest. The gray value of the center point of the image region of interest window is used as the threshold, and the other pixels in the image region of interest window are binarized to obtain the local texture features of the road surface image. Based on the local texture features of the road surface image, the image region of interest is divided into multiple grids, the gray-level co-occurrence matrix of each grid is calculated, the road surface texture features are extracted, and the road surface condition index of the vehicle's forward direction is generated. The formula is as follows: ,in, is the road surface condition index of the i-th grid, is a constant term, is the total number of dimensions of local texture features of all road images, For the The position of the local texture features of the road image, For the The degree of influence of the local texture features of the road surface image on the road surface condition, is the local binary pattern feature of the i-th grid, Where j is the dimension of the local binary pattern feature of the i-th grid, is the total number of all gray-level co-occurrence matrix features used, is the kth gray-level co-occurrence matrix feature, is the degree of influence of this feature on the road surface condition, Where k is the position of the feature in all gray-level co-occurrence matrix features, is the gray-level co-occurrence matrix feature of the i-th grid, The k in is the dimension of the gray-level co-occurrence matrix feature of the i-th grid, is the error term.
[0006] Preferably, a Pareto optimization algorithm is used to set initial upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and a road condition index in the vehicle's forward direction, randomly generate multiple solution sets within the adjustable range of the vehicle's electronically controlled suspension, calculate the multi-objective function value corresponding to each solution, sort them according to the Pareto ranking, and output the optimal solution set on the Pareto front within the adjustable range of the vehicle's electronically controlled suspension; Based on the optimal solution set of the Pareto front in the adjustable range of the vehicle's electronically controlled suspension, the optimal values of the corresponding multi-objective functions are calculated. Using the Euclidean distance algorithm, the optimal solution of the Pareto front in the adjustable range of the vehicle's electronically controlled suspension is selected, and the solution closest to the optimal value of the corresponding multi-objective function is selected. The final vehicle independent electronically controlled suspension parameters are output as follows: ,in, The optimal solution of the Pareto front in the adjustable range of the vehicle electronically controlled suspension is the closest solution to the optimal value of the corresponding multi-objective function. is the optimal value of the comfort objective function, is the comfort objective function value, is the optimal value of the passability objective function, is the passability objective function value, is the optimal value of the stability objective function, is the stability objective function value; Based on the final vehicle independent electronic control suspension parameters, an initialization control range value of the vehicle independent electronic control suspension is generated.
[0007] Preferably, based on the steering force feedback data of the vehicle driver, the real-time steering wheel steering torque and steering angular velocity of the vehicle driver are obtained, aligned with their timestamps, and the steering force data are preprocessed; Data preprocessing is performed based on the steering force data to extract the dynamic resistance torque in the steering force characteristics. The dynamic resistance torque in the steering force characteristics is calculated using a dynamic model, and the obtained resistance torque is normalized.
[0008] Preferably, based on normalizing the obtained resistance torque, an ideal road surface model is established to obtain an ideal road surface steering force, and a deviation value between the ideal road surface steering force and the actual steering force is calculated; According to the deviation between the ideal road steering force and the actual steering force, the weighted recursive filtering method is used to obtain the real-time road adhesion coefficient, correct the road condition index in the vehicle's forward direction, and obtain a real-time updated road condition index in the vehicle's forward direction.
[0009] Preferably, based on a road condition index that is updated in real time in the vehicle's forward direction, the damping coefficient is calculated according to the road adhesion coefficient. Low-adhesion roads reduce the damping force to avoid tire grip loss, while high-adhesion roads increase the damping force.
[0010] Preferably, a model predictive control method is used to establish a vehicle independent electronic suspension model, and an initial control range value is set according to the damping coefficient of the vehicle independent electronic suspension. Based on the road condition index of the vehicle's forward direction being updated in real time, the vehicle independent electronic suspension adjustment range is optimized to meet the minimum vehicle body vertical speed. The vehicle independent electronic suspension control parameters are calculated and adjusted in real time to obtain the vehicle independent electronic suspension range value to be controlled.
[0011] Preferably, based on the pressure data of each independent electronically controlled suspension of the vehicle and the range value of the independent electronically controlled suspension to be adjusted, the parameters of the independent electronically controlled suspension of the vehicle are obtained, the generalized coordinates of the independent electronically controlled suspension system of the vehicle are defined, the kinetic energy and potential energy functions of the independent electronically controlled suspension of the vehicle are established, the difference between the kinetic energy and potential energy functions of the independent electronically controlled suspension of the vehicle is calculated, and a Lagrangian function is established to obtain the motion equation of the independent electronically controlled suspension system of the vehicle; According to the motion equation of the vehicle's independent electronically controlled suspension system, the Euler method is used to perform numerical solution to obtain the dynamic response of the vehicle's independent electronically controlled suspension system.
[0012] Preferably, based on the dynamic response of the vehicle's independent electronically controlled suspension system, each vehicle's independent electronically controlled suspension independently runs a PID controller to meet the real-time update of the road condition index and vehicle body requirements in the vehicle's forward direction, and adjust the vehicle's independent electronically controlled suspension pressure according to the error; The vehicle independent electronic suspension pressure is adjusted based on the error. The control inputs of the four vehicle independent electronic suspensions are coordinated using multi-objective particle swarm optimization to obtain the optimal multi-point independent control electronic suspension system and generate a multi-point independent control electronic suspension system solution.
[0013] Furthermore, a multi-point independent control electronically controlled suspension system is provided for implementing the multi-point independent control electronically controlled suspension method described above, comprising: Road condition analysis module, initialization control range value module, road condition index correction module, to-be-controlled range value module and dynamic electronic suspension control model module The road surface condition analysis module is used to obtain real-time multi-source road surface data based on visual sensors to perform road surface smoothness analysis and generate road surface condition indicators for the vehicle's forward direction; The initialization control range value module is electrically connected to the road surface condition analysis module. The initialization control range value module is used to obtain the upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and perform correlation analysis with the road surface condition index in the vehicle's forward direction to generate an initialization control range value for the vehicle's independent electronically controlled suspension; The road surface condition index correction module is electrically connected to the road surface condition analysis module. The initialization road surface condition index correction module is used to obtain the steering force feedback data of the vehicle driver to correct the road surface condition index of the vehicle's forward direction in real time, thereby obtaining a real-time updated road surface condition index of the vehicle's forward direction. The to-be-regulated range value module is electrically connected to the regulation range value module. The to-be-regulated range value initialization module is used to correct the vehicle independent electronically controlled suspension initialization regulation range value using the road surface condition indicator that is updated in real time in the vehicle's forward direction, thereby obtaining the to-be-regulated range value of the vehicle independent electronically controlled suspension. The dynamic electronic suspension control model module is electrically connected to the range value module to be adjusted. The initialized dynamic electronic suspension control model module is used to obtain the pressure data of each independent electronic suspension of the vehicle and the range value to be adjusted of the independent electronic suspension of the vehicle, establish a dynamic electronic suspension control model, and generate a multi-point independent control electronic suspension system solution.
[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a multi-point independently controlled electronic suspension solution. By acquiring real-time road surface data based on a visual sensor and correlating it with the adjustable range of the electronic suspension for analysis, the suspension control range can be dynamically adjusted to optimize the vehicle's driving performance under different road conditions. By correcting road surface status indicators and suspension control range in real time, the adaptability and accuracy of the vehicle's suspension system are improved, and the vehicle's comfort, stability and controllability are enhanced, ensuring smooth driving of the vehicle in complex environments, and providing a more efficient multi-point independently controlled electronic suspension system. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 The present invention is a flow chart of a multi-point independent control electronic suspension method; Figure 2 This is a framework diagram of a multi-point independently controlled electronic suspension system. DETAILED DESCRIPTION
[0016] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.
[0017] Reference Figure 1 As shown, a multi-point independent control electronically controlled suspension method includes: Step 1: Based on the visual sensor, real-time multi-source road surface data is obtained to analyze the road surface roughness and generate road surface condition indicators for the vehicle's forward direction; The step 1 includes the following: Step 101: Based on the visual sensor, obtain real-time road surface multi-source data and perform road surface image preprocessing; Step 102: Based on the road surface image preprocessing, a local binary pattern is used to select a region of interest (ROI) in the image. The grayscale value of the center point of the ROI window is used as a threshold, and other pixels in the ROI window are binarized to obtain local texture features of the road surface image. Based on the local texture features of the road surface image, the image region of interest is divided into multiple grids, the gray-level co-occurrence matrix of each grid is calculated, the road surface texture features are extracted, and the road surface condition index of the vehicle's forward direction is generated. The formula is as follows: ,in, is the road surface condition index of the i-th grid, is a constant term, is the total number of dimensions of local texture features of all road images, For the The position of the local texture features of the road image, For the The degree of influence of the local texture features of the road surface image on the road surface condition, is the local binary pattern feature of the i-th grid, Where j is the dimension of the local binary pattern feature of the i-th grid, is the total number of all gray-level co-occurrence matrix features used, is the kth gray-level co-occurrence matrix feature, is the degree of influence of this feature on the road surface condition, Where k is the position of the feature in all gray-level co-occurrence matrix features, is the gray-level co-occurrence matrix feature of the i-th grid, The k in is the dimension of the gray-level co-occurrence matrix feature of the i-th grid, is the error term.
[0018] When using, combine the contents in steps 101 to 102. Real-time multi-source road surface data is acquired through visual sensors, and the road surface condition is analyzed in combination with image processing technology. The road surface image is preprocessed, and then the region of interest in the image is selected using local binary pattern (LBP) technology. The grayscale value of the center point of the region is used as the threshold, and other pixels are binarized to obtain local texture features. The image is divided into multiple grids, and the grayscale co-occurrence matrix is calculated for each grid to extract representative road surface texture features. Finally, a road surface condition indicator is generated that can be used to determine the direction in which the vehicle can move. Beneficial effects: This method can efficiently and accurately analyze the flatness and condition of the road surface, extract multi-source data through visual sensors, and evaluate the road surface condition in real time, thereby providing a reliable road surface condition indicator for adjusting the vehicle suspension system.
[0019] Step 2: Obtain the upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and perform correlation analysis with the road condition index in the vehicle's forward direction to generate an initialization control range value for the vehicle's independent electronically controlled suspension; The second step includes the following: Step 201: Using a Pareto optimization algorithm, initial upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and a road condition index in the vehicle's forward direction are set. Multiple solution sets are randomly generated within the adjustable range of the vehicle's electronically controlled suspension. The multi-objective function value corresponding to each solution is calculated and sorted according to the Pareto ranking. The optimal solution set on the Pareto front within the adjustable range of the vehicle's electronically controlled suspension is output. Based on the optimal solution set of the Pareto front in the adjustable range of the vehicle's electronically controlled suspension, the optimal values of the corresponding multi-objective functions are calculated. Using the Euclidean distance algorithm, the optimal solution of the Pareto front in the adjustable range of the vehicle's electronically controlled suspension is selected, and the solution closest to the optimal value of the corresponding multi-objective function is selected. The final vehicle independent electronically controlled suspension parameters are output as follows: ,in, The optimal solution of the Pareto front in the adjustable range of the vehicle electronically controlled suspension is the closest solution to the optimal value of the corresponding multi-objective function. is the optimal value of the comfort objective function, is the comfort objective function value, is the optimal value of the passability objective function, is the passability objective function value, is the optimal value of the stability objective function, is the stability objective function value; As a further content, the corresponding multi-objective function includes: comfort objective function, passability objective function, stability objective function; The final vehicle independent electronically controlled suspension parameters include: damping coefficient, travel height; Step 202: Generate an initialization control range value of the vehicle independent electronically controlled suspension based on the final vehicle independent electronically controlled suspension parameters.
[0020] When used, combine the contents in steps 201 to 202. Through the Pareto optimization algorithm, based on the relationship between the upper and lower limits of the adjustable range of the vehicle's electronic suspension and the road condition indicators, multiple solution sets are generated and their multi-objective function values are calculated; by sorting the solution sets according to the Pareto front, the optimal solution set is selected, and then the adjustable range of the vehicle's electronic suspension is determined; using the Euclidean distance algorithm, the closest optimal solution is further selected to obtain the final vehicle independent electronic suspension parameters, such as damping coefficient and travel height; the initialization control range value is generated based on these parameters; this method can comprehensively consider multiple objective factors such as comfort, passability and stability, optimize the vehicle suspension system, and improve the driving experience; the beneficial effects include: accurately optimizing the vehicle suspension system parameters, improving comfort, passability and stability, enhancing the vehicle's ability to adapt to different road conditions, and thus improving the vehicle's overall performance and driving safety.
[0021] Step 3: Obtain steering force feedback data from the vehicle driver to correct the road surface condition indicator of the vehicle's forward direction in real time, thereby obtaining a road surface condition indicator that updates the vehicle's forward direction in real time; The step three includes the following: Step 301: Based on the steering force feedback data of the vehicle driver, obtain the real-time steering wheel steering torque and steering angular velocity of the vehicle driver, align them with their timestamps, and perform data preprocessing on the steering force data; Step 302: Preprocess the steering force data to extract the dynamic resistance torque in the steering force characteristics, calculate the dynamic resistance torque in the steering force characteristics using a dynamic model, and normalize the obtained resistance torque. Step 303: Based on the normalization of the obtained resistance torque, an ideal road surface model is established to obtain the ideal road surface steering force, and the deviation between the ideal road surface steering force and the actual steering force is calculated; According to the deviation between the ideal road steering force and the actual steering force, the weighted recursive filtering method is used to obtain the real-time road adhesion coefficient, correct the road condition index in the vehicle's forward direction, and obtain a real-time updated road condition index in the vehicle's forward direction.
[0022] When used, combine the contents in steps 301 to 303. By collecting and preprocessing the steering force feedback data of vehicle drivers, extracting the dynamic resistance torque characteristics and normalizing them, an ideal road surface model is established; then, by calculating the deviation between the ideal road surface steering force and the actual steering force, the road surface condition indicators are corrected using the weighted recursive filtering method, and the road surface condition in the vehicle's forward direction is updated in real time; through this process, the road surface condition can be accurately perceived and corrected, improving driving safety and optimizing vehicle control performance, ensuring stable driving of the vehicle under complex road conditions.
[0023] Step 4: Using the road condition indicator updated in real time in the vehicle's forward direction, the initialization control range value of the vehicle's independent electronically controlled suspension is corrected to obtain the to-be-controlled range value of the vehicle's independent electronically controlled suspension; The step 4 includes the following contents: Step 401: Based on a road condition indicator updated in real time in the vehicle's forward direction, a damping coefficient is calculated according to the road adhesion coefficient. The damping force is reduced on low-adhesion roads to avoid tire grip loss, and the damping force is increased on high-adhesion roads. Step 402: Utilize the model predictive control method to establish a vehicle independent electronic suspension model, set an initial control range value based on the vehicle independent electronic suspension damping coefficient, optimize the vehicle independent electronic suspension adjustment range to minimize the vehicle body vertical speed based on the road condition index updated in real time in the vehicle's forward direction, calculate and adjust the vehicle independent electronic suspension control parameters in real time, and obtain the vehicle independent electronic suspension adjustment range value.
[0024] When used, combine the contents in steps 401 to 402. The damping coefficient of the suspension system is dynamically adjusted based on real-time updated road condition indicators and the vehicle's forward adhesion coefficient. The damping force is reduced on low-adhesion roads to prevent tire grip loss, while the damping force is increased on high-adhesion roads to enhance stability. Utilizing the Model Predictive Control (MPC) method, an independent electronic suspension model of the vehicle is established to optimize the suspension's control range based on real-time road conditions. This process not only adjusts the suspension's control parameters in real time to minimize the vehicle's vertical velocity, thereby improving ride comfort, but also effectively adapts to different road conditions, enhancing the vehicle's driving stability and safety. The beneficial effects of this method include more precise suspension adjustment, a better driving experience, and improved vehicle adaptability and stability under various road conditions.
[0025] Step 5: Obtain the pressure data of each independent electronically controlled suspension of the vehicle and the range value to be adjusted of the independent electronically controlled suspension of the vehicle, establish a dynamic electronically controlled suspension control model, and generate a multi-point independent control electronically controlled suspension system solution; The step five includes the following: Step 501: Based on the pressure data of each independent electronically controlled suspension of the vehicle and the range of the independent electronically controlled suspension to be adjusted, the parameters of the independent electronically controlled suspension of the vehicle are obtained, the generalized coordinates of the independent electronically controlled suspension system of the vehicle are defined, the kinetic energy and potential energy functions of the independent electronically controlled suspension of the vehicle are established, the difference between the kinetic energy and potential energy functions of the independent electronically controlled suspension of the vehicle is calculated, and a Lagrangian function is established to obtain the motion equation of the independent electronically controlled suspension system of the vehicle; According to the motion equation of the vehicle's independent electronically controlled suspension system, the Euler method is used to perform numerical solution to obtain the dynamic response of the vehicle's independent electronically controlled suspension system. Step 502: Based on the dynamic response of the independent electronically controlled suspension system, each independent electronically controlled suspension of the vehicle independently runs a PID controller to meet the real-time update of the road condition index and vehicle body requirements in the vehicle's forward direction, and adjusts the pressure of the independent electronically controlled suspension of the vehicle according to the error; Step 503: Adjust the pressure of the vehicle's independent electronic suspension based on the error, use multi-objective particle swarm optimization to coordinate the control inputs of the four vehicle's independent electronic suspensions, obtain the optimal multi-point independent control electronic suspension system, and generate a multi-point independent control electronic suspension system solution.
[0026] When used, combine the contents in steps 501 to 503. A dynamic electronic suspension control model is established by comprehensively analyzing the pressure data and control range of each independent electronic suspension system. Based on the pressure data and control range of the vehicle suspension system, the system's generalized coordinates are defined, and kinetic and potential energy functions are established. The Lagrangian function is further derived, resulting in the suspension system's equation of motion. The equation of motion is numerically solved using the Euler method to obtain the system's dynamic response. Based on this dynamic response, an independently running PID controller is used to adjust the suspension pressure in real time to respond to road surface changes and vehicle body requirements. Finally, a multi-objective particle swarm optimization algorithm is used to coordinate the control inputs of the four independent electronic suspensions to obtain the optimal multi-point independent control solution, thereby achieving precise adjustment and optimization of the suspension system. By precisely controlling the suspension pressure, vehicle stability and comfort are improved, intelligent regulation of the suspension system is achieved, and the optimal balance between dynamic performance and driving experience is ensured.
[0027] Reference Figure 2 As shown, a multi-point independent control electronically controlled suspension system includes: Road condition analysis module, initialization control range value module, road condition index correction module, to-be-controlled range value module and dynamic electronic suspension control model module The road surface condition analysis module is used to obtain real-time multi-source road surface data based on visual sensors to perform road surface smoothness analysis and generate road surface condition indicators for the vehicle's forward direction; The initialization control range value module is electrically connected to the road surface condition analysis module. The initialization control range value module is used to obtain the upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and perform correlation analysis with the road surface condition index in the vehicle's forward direction to generate an initialization control range value for the vehicle's independent electronically controlled suspension; The road surface condition index correction module is electrically connected to the road surface condition analysis module. The initialization road surface condition index correction module is used to obtain the steering force feedback data of the vehicle driver to correct the road surface condition index of the vehicle's forward direction in real time, thereby obtaining a real-time updated road surface condition index of the vehicle's forward direction. The to-be-regulated range value module is electrically connected to the regulation range value module. The to-be-regulated range value initialization module is used to correct the vehicle independent electronically controlled suspension initialization regulation range value using the road surface condition indicator that is updated in real time in the vehicle's forward direction, thereby obtaining the to-be-regulated range value of the vehicle independent electronically controlled suspension. The dynamic electronic suspension control model module is electrically connected to the range value module to be adjusted. The initialized dynamic electronic suspension control model module is used to obtain the pressure data of each independent electronic suspension of the vehicle and the range value to be adjusted of the independent electronic suspension of the vehicle, establish a dynamic electronic suspension control model, and generate a multi-point independent control electronic suspension system solution.
[0028] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.
Claims
1. A multi-point independent control electronic suspension method, characterized in that: include: S1. Based on visual sensors, real-time multi-source road surface data can be obtained to analyze road surface roughness and generate road surface condition indicators for the vehicle's forward direction; S2. Obtaining upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and performing correlation analysis with road condition indicators in the vehicle's forward direction to generate an initialization control range value for the vehicle's independent electronically controlled suspension; S3. Obtaining steering force feedback data from the vehicle driver to correct the road surface condition indicator of the vehicle's forward direction in real time, thereby obtaining a road surface condition indicator that is updated in real time in the vehicle's forward direction; S4. Correcting the initialization control range value of the vehicle's independent electronically controlled suspension using the road surface condition indicator updated in real time in the vehicle's forward direction to obtain a to-be-controlled range value of the vehicle's independent electronically controlled suspension; S5. Obtain pressure data of each independent electronically controlled suspension of the vehicle and a range value of the independent electronically controlled suspension to be adjusted, establish a dynamic electronically controlled suspension control model, and generate a multi-point independently controlled electronically controlled suspension system solution.
2. The multi-point independent control electronic suspension method and system according to claim 1, characterized in that: Said S1 comprises: Based on visual sensors, real-time multi-source road data is obtained for road image preprocessing; Based on the road surface image preprocessing, the local binary pattern is used to select the image region of interest. The gray value of the center point of the image region of interest window is used as the threshold, and the other pixels in the image region of interest window are binarized to obtain the local texture features of the road surface image. Based on the local texture features of the road surface image, the image region of interest is divided into multiple grids, the gray-level co-occurrence matrix of each grid is calculated, the road surface texture features are extracted, and the road surface condition index of the vehicle's forward direction is generated. The formula is as follows: ,in, is the road surface condition index of the i-th grid, is a constant term, is the total number of dimensions of local texture features of all road images, For the The position of the local texture features of the road image, For the The degree of influence of the local texture features of the road surface image on the road surface condition, is the local binary pattern feature of the i-th grid, Where j is the dimension of the local binary pattern feature of the i-th grid, is the total number of all gray-level co-occurrence matrix features used, is the kth gray-level co-occurrence matrix feature, is the degree of influence of this feature on the road surface condition, Where k is the position of the feature in all gray-level co-occurrence matrix features, is the gray-level co-occurrence matrix feature of the i-th grid, The k in is the dimension of the gray-level co-occurrence matrix feature of the i-th grid, is the error term.
3. The multi-point independent control electronic suspension method according to claim 1, characterized in that: The S2 includes: Using the Pareto optimization algorithm, the initial upper and lower limits of the vehicle's electronically controlled suspension's adjustable range and the road condition index in the vehicle's forward direction are set. Multiple solution sets are randomly generated within the vehicle's electronically controlled suspension's adjustable range. The multi-objective function value corresponding to each solution is calculated and sorted according to the Pareto ranking. The optimal solution set on the Pareto front within the vehicle's electronically controlled suspension's adjustable range is output. Based on the optimal solution set of the Pareto front in the adjustable range of the vehicle's electronically controlled suspension, the optimal values of the corresponding multi-objective functions are calculated. Using the Euclidean distance algorithm, the optimal solution of the Pareto front in the adjustable range of the vehicle's electronically controlled suspension is selected, and the solution closest to the optimal value of the corresponding multi-objective function is selected. The final vehicle independent electronically controlled suspension parameters are output as follows: ,in, The optimal solution of the Pareto front in the adjustable range of the vehicle electronically controlled suspension is the closest solution to the optimal value of the corresponding multi-objective function. is the optimal value of the comfort objective function, is the comfort objective function value, is the optimal value of the passability objective function, is the passability objective function value, is the optimal value of the stability objective function, is the stability objective function value; Based on the final vehicle independent electronic control suspension parameters, an initialization control range value of the vehicle independent electronic control suspension is generated.
4. The multi-point independent control electronic suspension method according to claim 1, characterized in that: The S3 includes: Based on the steering force feedback data of the vehicle driver, the real-time steering wheel steering torque and steering angular velocity of the vehicle driver are obtained, aligned with their timestamps, and the steering force data are preprocessed; Data preprocessing is performed based on the steering force data to extract the dynamic resistance torque in the steering force characteristics. The dynamic resistance torque in the steering force characteristics is calculated using a dynamic model, and the obtained resistance torque is normalized.
5. The multi-point independent control electronic suspension method according to claim 4, characterized in that: Said S3 further comprises: Based on the normalization of the obtained resistance torque, an ideal road model is established to obtain the ideal road steering force, and the deviation between the ideal road steering force and the actual steering force is calculated; According to the deviation between the ideal road steering force and the actual steering force, the weighted recursive filtering method is used to obtain the real-time road adhesion coefficient, correct the road condition index in the vehicle's forward direction, and obtain a real-time updated road condition index in the vehicle's forward direction.
6. The multi-point independent control electronic suspension method according to claim 1, characterized in that: The S4 includes: Based on the real-time update of the road condition index in the vehicle's forward direction, the damping coefficient is calculated according to the road adhesion coefficient. Low-adhesion roads reduce the damping force to avoid loss of tire grip, while high-adhesion roads increase the damping force.
7. The multi-point independent control electronic suspension method according to claim 6, characterized in that: Said S4 further comprises: Using the model predictive control method, a vehicle independent electronic suspension model is established. The initial control range value is set according to the damping coefficient of the vehicle independent electronic suspension. Based on the real-time update of the road condition index in the vehicle's forward direction, the vehicle independent electronic suspension adjustment range is optimized to minimize the vertical speed of the vehicle body. The vehicle independent electronic suspension control parameters are calculated and adjusted in real time to obtain the vehicle independent electronic suspension's control range value.
8. The multi-point independent control electronic suspension method according to claim 1, characterized in that: The S5 includes: Based on the pressure data of each independent electronically controlled suspension of the vehicle and the range value of the independent electronically controlled suspension to be adjusted, the parameters of the independent electronically controlled suspension of the vehicle are obtained, the generalized coordinates of the independent electronically controlled suspension system of the vehicle are defined, the kinetic energy and potential energy functions of the independent electronically controlled suspension of the vehicle are established, the difference between the kinetic energy and potential energy functions of the independent electronically controlled suspension of the vehicle is calculated, and the Lagrangian function is established to obtain the motion equation of the independent electronically controlled suspension system of the vehicle; According to the motion equation of the vehicle's independent electronically controlled suspension system, the Euler method is used to perform numerical solution to obtain the dynamic response of the vehicle's independent electronically controlled suspension system.
9. The multi-point independent control electronic suspension method according to claim 8, characterized in that: The S5 further includes: Based on the dynamic response of the vehicle's independent electronic suspension system, each vehicle's independent electronic suspension independently runs a PID controller to meet the real-time update of road condition indicators and vehicle body requirements in the vehicle's forward direction, and adjust the vehicle's independent electronic suspension pressure according to the error; The vehicle independent electronic suspension pressure is adjusted based on the error. The control inputs of the four vehicle independent electronic suspensions are coordinated using multi-objective particle swarm optimization to obtain the optimal multi-point independent control electronic suspension system and generate a multi-point independent control electronic suspension system solution.
10. A multi-point independent control electronic suspension system, characterized in that: The method for implementing any one of claims 1 to 9, wherein the method comprises: Road condition analysis module, initialization control range value module, road condition index correction module, to-be-controlled range value module and dynamic electronic suspension control model module The road surface condition analysis module is used to obtain real-time multi-source road surface data based on visual sensors to perform road surface smoothness analysis and generate road surface condition indicators for the vehicle's forward direction; The initialization control range value module is electrically connected to the road surface condition analysis module. The initialization control range value module is used to obtain the upper and lower limits of the adjustable range of the vehicle's electronically controlled suspension and perform correlation analysis with the road surface condition index in the vehicle's forward direction to generate an initialization control range value for the vehicle's independent electronically controlled suspension; The road surface condition index correction module is electrically connected to the road surface condition analysis module. The initialization road surface condition index correction module is used to obtain the steering force feedback data of the vehicle driver to correct the road surface condition index of the vehicle's forward direction in real time, thereby obtaining a real-time updated road surface condition index of the vehicle's forward direction. The to-be-regulated range value module is electrically connected to the regulation range value module. The to-be-regulated range value initialization module is used to correct the vehicle independent electronically controlled suspension initialization regulation range value using the road surface condition indicator that is updated in real time in the vehicle's forward direction, thereby obtaining the to-be-regulated range value of the vehicle independent electronically controlled suspension. The dynamic electronic suspension control model module is electrically connected to the range value module to be adjusted. The initialized dynamic electronic suspension control model module is used to obtain the pressure data of each independent electronic suspension of the vehicle and the range value to be adjusted of the independent electronic suspension of the vehicle, establish a dynamic electronic suspension control model, and generate a multi-point independent control electronic suspension system solution.