Vehicle control method and device, electronic equipment and medium

Abstract

The invention provides a vehicle control method and device, electronic equipment and a medium, and relates to the field of vehicle control. Comprises: obtaining vehicle operation data; determining a vehicle body operation stability degree based on the vehicle operation data; based on the damping control parameters and a control strategy of the vehicle body operation stability, self-adaptive stability optimization processing is conducted on the vehicle body operation stability degree; and based on a control strategy of the damping control parameters and the vehicle body operation stability, a damping control parameter adjustment scheme for reducing the difference between the vehicle body operation stability degree and the stability target until the stability target is met is determined, and the adjustment process of the damping control parameters of the vehicle chassis is controlled so that the vehicle body can reach the stability target. According to the method, self-adaptive adjustment of the damping force of the vehicle chassis is achieved, the driving comfort of the vehicle is improved, and compared with control through table look-up, the accuracy is better, and the efficiency is higher.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle control, and more particularly to a vehicle control method and apparatus, electronic equipment and medium. Background Technology

[0002] The damping force of the vehicle chassis CDC affects the driving comfort experience. Under different damping forces, the vehicle will exhibit different states, thus allowing the driver and passengers to experience different things during the vehicle's operation. Summary of the Invention

[0003] This disclosure provides a vehicle control method, apparatus, electronic device, and medium to achieve the purpose of controlling the damping force of the vehicle chassis through adaptive adjustment of vehicle control parameters.

[0004] The first aspect of this disclosure provides a vehicle control method, comprising: acquiring vehicle operating data, wherein the vehicle operating data is the operating data of the vehicle under the damping force of the vehicle chassis, and the operating data reflects the vehicle body operating state under the damping force of the vehicle chassis; determining the vehicle body operating stability based on the vehicle operating data; performing adaptive stability optimization processing on the vehicle body operating stability based on the damping control parameters and the control strategy of vehicle body operating stability; determining a damping control parameter adjustment scheme to narrow the gap between the vehicle body operating stability and the stability target until the stability target is met, based on the damping control parameters and the control strategy of vehicle body operating stability; and controlling the adjustment process of the damping control parameters of the vehicle chassis so that the vehicle body achieves the stability target.

[0005] In the above embodiments, the vehicle's stability is quantified, and parameter adjustment schemes are automatically generated based on intelligent control strategies, dynamically optimizing until the target is achieved. This transforms traditional manual calibration into automatic optimization, improving efficiency and consistency, and enabling the vehicle to adaptively maintain optimal stability.

[0006] In some embodiments of this disclosure, vehicle operating data includes vehicle body acceleration, vehicle body attitude data, and vehicle chassis vertical acceleration; vehicle body attitude data includes at least one of the following: roll angle; yaw angle; pitch angle.

[0007] In the above embodiments, a multi-dimensional vehicle body state perception system is established by clearly defining the data composition. By integrating vibration and attitude data, the three-dimensional dynamic response of the vehicle is comprehensively characterized, providing a complete data foundation for accurate stability assessment and avoiding evaluation biases caused by single-dimensional perception.

[0008] In some embodiments of this disclosure, the vehicle body running stability is determined based on vehicle operating data, including: determining the vehicle body vibration level based on vehicle body acceleration, and determining the attitude angle fluctuation level based on vehicle body attitude data; and determining the vehicle body running stability based on the vehicle body vibration level and the attitude angle fluctuation level.

[0009] In the above embodiments, stability is decomposed into two dimensions—vibration and attitude—for quantitative evaluation, thereby objectifying subjective feelings. This provides refined performance diagnostics, distinguishing between comfort and handling issues, and pointing the way for targeted optimization.

[0010] In some embodiments of this disclosure, the vehicle running stability is determined based on the degree of vehicle vibration and the degree of attitude angle fluctuation, including: determining a first weighting coefficient corresponding to the degree of vehicle vibration and a second weighting coefficient corresponding to the degree of attitude angle fluctuation; and calculating a weighted sum of squares of the degree of vehicle vibration and the degree of attitude angle fluctuation based on the first weighting coefficient and the second weighting coefficient to obtain the degree of vehicle running stability.

[0011] In the above embodiments, a weighted sum of squares calculation is used to amplify the representation of unstable states. Configurable weighting coefficients provide a performance trade-off lever, supporting differentiated definitions of stability priorities and providing underlying support for implementing different driving modes.

[0012] In some embodiments of this disclosure, determining a first weighting coefficient corresponding to the degree of vehicle body vibration and a second weighting coefficient corresponding to the degree of attitude angle fluctuation includes: determining the vehicle driving mode; and based on the vehicle driving mode, determining a set of weighting coefficients associated with the vehicle driving mode, the set of weighting coefficients including the first weighting coefficient and the second weighting coefficient.

[0013] In the above embodiments, parameter tuning is transformed into a signal tracking control problem. The optimal damping adjustment value is calculated in real time based on the error signal, achieving dynamic correction. This exhibits adaptability, does not rely on a precise model, and automatically adjusts the strategy solely based on feedback.

[0014] In some embodiments of this disclosure, an adaptive stability optimization process is performed on the vehicle's running stability based on a control strategy that considers damping control parameters and vehicle running stability. This includes: determining the damping control parameters to be adjusted based on the vehicle's running stability and the vehicle chassis vertical acceleration through adaptive filtering; adjusting the initial damping control parameters based on the damping control parameters to be adjusted; and determining the vehicle's running stability under the adjusted vehicle chassis damping force.

[0015] In some embodiments of this disclosure, based on the vehicle body's running stability and the vehicle chassis's vertical acceleration, adaptive filtering is used to determine the damping control parameters to be adjusted, including: determining the filter weights based on the vehicle body's running stability, the vehicle chassis's vertical acceleration, and preset coefficients; and determining the damping control parameters to be adjusted based on the vehicle chassis's vertical acceleration and the filter weights.

[0016] In the above embodiments, clear rules for online weight adjustment are provided. By combining performance errors and interference inputs to drive weight iteration, the filter characteristics dynamically adapt to system performance, improving the convergence speed and robustness of the optimization process.

[0017] In some embodiments of this disclosure, the filtering weights are determined based on the vehicle body running stability, the vehicle chassis vertical acceleration, and a preset coefficient, including: determining the initial filtering weights; and adding the product of the vehicle body running stability, the calculated value of the vehicle chassis vertical acceleration, and the preset coefficients with the initial filtering weights to determine the filtering weights.

[0018] In the above embodiments, addition is used to update weights, which is simple, effective, and easy to deploy. It ensures smooth and bounded updates, balances adaptability and system stability, and facilitates engineering implementation.

[0019] In some embodiments of this disclosure, determining the damping control parameters to be adjusted based on the vehicle chassis vertical acceleration and filter weights includes: performing a convolution operation between the vehicle chassis vertical acceleration and the filter weights to determine the damping control parameters to be adjusted.

[0020] In the above embodiments, real-time filtering control is achieved through convolution operations, transforming adaptive logic into executable instructions. This reflects the physical essence of CDC control: the optimal damping force is calculated based on wheel motion using a digital filter, a process that is physically interpretable.

[0021] In some embodiments of this disclosure, based on the control strategy of damping control parameters and vehicle body running stability, a damping control parameter adjustment scheme is determined to narrow the gap between the vehicle body running stability and the stability target until the stability target is met. The process of adjusting the damping control parameters of the vehicle chassis is controlled so that the vehicle body achieves the stability target. This includes: responding to the fact that the gap between the re-determined vehicle body running stability and the stability target does not meet the stability target, based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, and re-determining the damping control parameters to be adjusted through adaptive filtering; and adjusting the damping force of the vehicle chassis on which the vehicle is located based on the re-determined damping control parameters to be adjusted until the gap between the vehicle body running stability and the stability target is narrowed to meet the stability target, thus determining the damping control parameter adjustment scheme.

[0022] In the above embodiments, a complete iterative closed loop of "perception-evaluation-decision-execution" is formed. This ensures the convergence and goal-orientation of the method, continuously optimizing until the target is met. The automated calibration process efficiently and systematically finds the optimal parameter scheme.

[0023] In the above embodiments, by fusing multi-dimensional dynamic data such as vehicle acceleration and attitude angles in real time, a vehicle running stability level that balances comfort and stability is constructed. The difference between this level and the stability target is used as the objective to drive an adaptive filtering process that includes secondary path compensation to iteratively optimize control parameters online. This method achieves end-to-end intelligent decision-making from road excitation perception and vehicle state assessment to precise damping force adjustment, enabling the suspension system to actively and accurately counteract vibrations. Without requiring a precise vehicle model, it dynamically balances ride comfort and handling stability, improving the vehicle's adaptability to different road conditions and loads.

[0024] A second aspect of this disclosure provides a vehicle control device, comprising: a data acquisition module, a determination module, a processing module, and a control module. The data acquisition module acquires vehicle operating data, which is the vehicle's operating data under the damping force of its chassis, reflecting the vehicle's body operating state under the damping force. The determination module determines the vehicle's operating stability based on the vehicle operating data. The processing module performs adaptive stability optimization processing on the vehicle's operating stability based on the damping control parameters and the control strategy for vehicle operating stability. The control module determines a damping control parameter adjustment scheme to narrow the gap between the vehicle's operating stability and the stability target until the stability target is met, based on the damping control parameters and the control strategy for vehicle operating stability, and controls the adjustment process of the vehicle chassis's damping control parameters to ensure that the vehicle body achieves the stability target.

[0025] In some embodiments of this disclosure, vehicle operating data includes vehicle body acceleration, vehicle body attitude data, and vehicle chassis vertical acceleration; vehicle body attitude data includes at least one of the following: roll angle; yaw angle; pitch angle.

[0026] In some embodiments of this disclosure, the determining module is used to: determine the degree of vehicle vibration based on vehicle acceleration, and determine the degree of attitude angle fluctuation based on vehicle attitude data; and determine the degree of vehicle running stability based on the degree of vehicle vibration and the degree of attitude angle fluctuation.

[0027] In some embodiments of this disclosure, the determining module is used to: determine a first weighting coefficient corresponding to the degree of vehicle vibration and a second weighting coefficient corresponding to the degree of attitude angle fluctuation; and calculate the weighted sum of squares of the degree of vehicle vibration and the degree of attitude angle fluctuation based on the first weighting coefficient and the second weighting coefficient to obtain the degree of vehicle running stability.

[0028] In some embodiments of this disclosure, the determining module is used to: determine the vehicle driving mode; and based on the vehicle driving mode, determine a set of weight coefficients associated with the vehicle driving mode, the set of weight coefficients including a first weight coefficient and a second weight coefficient.

[0029] In some embodiments of this disclosure, the processing module is used to: determine the damping control parameters to be adjusted based on the vehicle body running stability and the vehicle chassis vertical acceleration through adaptive filtering; adjust the initial damping control parameters based on the damping control parameters to be adjusted; and determine the vehicle body running stability under the adjusted vehicle chassis damping force.

[0030] In some embodiments of this disclosure, the processing module is used to: determine the filter weights based on the vehicle body running stability, the vehicle chassis vertical acceleration, and a preset coefficient; and determine the damping control parameters to be adjusted based on the vehicle chassis vertical acceleration and the filter weights.

[0031] In some embodiments of this disclosure, the processing module is used to: determine the initial filter weights; and perform an addition operation on the product of the vehicle body running stability, the calculated value of the vehicle chassis vertical acceleration, and the preset coefficient with the initial filter weights to determine the filter weights.

[0032] In some embodiments of this disclosure, the processing module is used to: perform a convolution operation on the vertical acceleration of the vehicle chassis and the filter weights to determine the damping control parameters to be adjusted.

[0033] In some embodiments of this disclosure, the control module is configured to: respond to a situation where the difference between the redefined vehicle body running stability and the stability target does not meet the stability target, based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, and through adaptive filtering, redetermine the damping control parameters to be adjusted; based on the redefined damping control parameters to be adjusted, adjust the damping force of the vehicle chassis on which the vehicle is located until the difference between the vehicle body running stability and the stability target is reduced to meet the stability target, and determine a damping control parameter adjustment scheme.

[0034] In the above embodiments, the vehicle control device can achieve precise adjustment of damping force based on road excitation perception and vehicle body state assessment, enabling the suspension system to actively and accurately counteract vibrations. During real-time vehicle operation, it dynamically balances driving comfort and handling stability, improving the vehicle's adaptability to different road conditions and loads.

[0035] A third aspect of this disclosure provides a vehicle configured to perform the vehicle control method described in any embodiment of the first aspect of this disclosure, or to include the vehicle control apparatus described in any embodiment of the second aspect of this disclosure.

[0036] A fourth aspect of this disclosure provides an electronic device including: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor performs the method described in any of the embodiments of the first aspect of this disclosure when running the computer program.

[0037] A fifth aspect of this disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to perform the methods described in any of the embodiments of the first aspect of this disclosure.

[0038] A sixth aspect of this disclosure provides a chip including at least one processor and a communication interface; the communication interface is used to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method described in any embodiment of the first aspect of this disclosure through logic circuits or executing code instructions.

[0039] In summary, the vehicle control method proposed in this disclosure constructs a comprehensive state error that balances comfort and stability by fusing multi-dimensional dynamic data such as vehicle acceleration and attitude angles in real time. Using this error as the target, it drives an adaptive filtering process that includes secondary path compensation to iteratively optimize control parameters online. This method achieves end-to-end intelligent decision-making from road excitation perception and vehicle state assessment to precise damping force adjustment, enabling the suspension system to actively and accurately counteract vibrations. Without requiring a precise vehicle model, it dynamically balances ride comfort and handling stability, improving the vehicle's adaptability to different road conditions and loads.

[0040] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0042] Figure 1 A schematic flowchart of a vehicle control method provided in an embodiment of this disclosure; Figure 2 This is a schematic flowchart of another vehicle control method proposed in an embodiment of this disclosure; Figure 3 This is a schematic flowchart of another vehicle control method proposed in an embodiment of this disclosure; Figure 4 This is a schematic flowchart of another vehicle control method proposed in an embodiment of this disclosure; Figure 5 This is a flowchart illustrating a vehicle chassis adaptive control system. Figure 6 This is a schematic diagram of the vehicle control device proposed in the embodiments of this disclosure; Figure 7 A block diagram illustrating a vehicle 700 according to an exemplary embodiment; Figure 8 This is a schematic diagram of the structure of a chip for implementing a vehicle control method according to an exemplary embodiment. Detailed Implementation

[0043] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated 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 disclosure, and should not be construed as limiting this disclosure.

[0044] The calibration of CDC (Continuous Damping Control) damping systems in vehicle chassis involves a massive number of parameters. It requires determining the control current of solenoid valves for different vehicle speeds and attitudes, with these parameters being coupled and constrained. In related technologies, CDC damping force parameter tables require calibration for various styles (Comfort, Sport, Sport+), each needing calibration for 15 typical operating conditions (e.g., using a dcbase + skyhook control strategy for straight-line driving). Each operating condition has 8-15 tables, each with over 100 parameters. Given this enormous parameter calibration workload, chassis tuning engineers primarily confirm the optimal effect through real-vehicle testing and subjective evaluation. Calibrating all parameters for each style to the subjectively optimal level takes 5-8 weeks, resulting in high testing costs.

[0045] The method of obtaining chassis control by looking up the table after obtaining the calibrated damping force parameter table has many drawbacks, such as control lag, inability to accurately calibrate parameters, and significant reduction in road comfort and handling outside of typical working conditions.

[0046] Therefore, in order to solve the problems of control lag and inaccurate calibration parameters caused by the need to look up tables to adjust the damping force of the vehicle chassis, this disclosure proposes an adaptive vehicle control method that applies adaptive filtering technology from active noise reduction systems to intelligent chassis control.

[0047] The vehicle control method provided in this application will now be described in detail with reference to the accompanying drawings.

[0048] The vehicle control method disclosed herein can be applied to vehicle control systems.

[0049] Figure 1 This is a schematic flowchart of a vehicle control method proposed in an embodiment of this disclosure. Figure 1 As shown, the steps include the following.

[0050] Step 101: Obtain vehicle operation data.

[0051] In some embodiments, vehicle operating data refers to the operating data of the vehicle under the damping force of the vehicle chassis, and the operating data reflects the vehicle body operating state under the damping force of the vehicle chassis.

[0052] In some embodiments, the vehicle chassis damping force can be the chassis control effect achieved by the vehicle under the control of initial damping control parameters, or it can be the chassis control effect achieved by controlling the vehicle after the initial damping control parameters have been adjusted. In other words, the vehicle operating data represents the chassis control effect achieved after each adjustment of the damping control parameters during multiple adjustments.

[0053] In some embodiments, when the vehicle is under the control of initial damping control parameters, the collected operating data reflects the vehicle body operating state under initial damping force.

[0054] In some embodiments, the initial damping force can be the damping force under real-time control of the damping control parameters currently used during vehicle operation. Specifically, the damping control parameters currently used can be a set of control parameters determined by looking up the damping force parameter table at the current moment. This set of parameters can be the reference value in the initial damping force parameter table, or the parameter value in the damping force parameter table determined by calibration or adjustment.

[0055] Furthermore, the damping force parameter table can be calibrated or adjusted manually, or it can be calibrated or adjusted intelligently and automatically through an evaluation model to determine the target value of each damping control parameter in the damping force parameter table.

[0056] In some embodiments, vehicle operation data can be obtained by real-time sensing and detection through sensors installed on the vehicle, reflecting the vehicle's body operation status under the control of current damping control parameters.

[0057] In some embodiments, vehicle operation data may include operation data corresponding to the vehicle's lower body and upper body. Specifically, the vehicle's lower body can reflect the operating status of the vehicle chassis, while the vehicle's upper body can reflect the vehicle's body posture under the current damping force. Thus, the combined effect of the upper and lower body may affect the overall experience and comfort of the passengers sitting inside the vehicle.

[0058] In some embodiments, vehicle operating data includes vehicle body acceleration, vehicle body attitude data, and vehicle chassis vertical acceleration; vehicle body attitude data includes at least one of the following: roll angle; yaw angle; pitch angle.

[0059] In some embodiments, vehicle body acceleration refers to the rate of change of velocity of the vehicle body (sprung mass) in a certain direction. Under suspension control, the most crucial factor is "vertical acceleration," which serves as a core indicator for directly measuring ride comfort. A smaller vertical acceleration means a smoother vehicle body and less noticeable vertical bumps experienced by passengers. One of the main goals of the control system is to suppress this acceleration.

[0060] In some embodiments, vehicle chassis vertical acceleration refers to the rate of change of velocity of the vehicle chassis, wheels, or unsprung mass in the vertical direction. It directly reflects the impact and excitation caused by uneven road surfaces. It is equivalent to "interference source" information; the control system needs to use this signal to predict and counteract its impact on the vehicle body. It is also an indirect indicator for evaluating tire contact performance.

[0061] In the above embodiments, the vertical acceleration of the vehicle chassis is one cause of changes in vehicle body acceleration. Road impacts (i.e., vertical acceleration of the vehicle chassis) are first transmitted through the tires and suspension before causing vibration (vehicle body acceleration) in the vehicle body. The suspension system acts like a "filter," aiming to minimize the conversion of severe chassis acceleration into vehicle body acceleration.

[0062] In some embodiments, vehicle posture data describes the angular changes of the vehicle body relative to a horizontal reference, and is key to measuring vehicle handling stability and safety.

[0063] Specifically, roll angle refers to the angle at which a vehicle body rotates around its front-to-rear axis (X-axis). It can also be called "puff angle," for example, when a vehicle is turning, due to centrifugal force, the body leans outwards, creating a roll angle. Excessive roll angle can make passengers feel uneasy, affecting comfort, and means increased tire load transfer, potentially reducing handling limits. Control systems need to suppress body roll during cornering.

[0064] Specifically, pitch angle refers to the angle at which the vehicle body rotates around its left-right axis (Y-axis). It can also be referred to as the "nodding" or "tilting" angle. For example, during emergency braking, the front of the car pitches down (nodding), and during rapid acceleration, the front of the car lifts up (tilting). Suppressing pitch angle can improve vehicle stability during braking and acceleration, maintain vehicle stability, and reduce the feeling of forward and backward swaying for passengers.

[0065] Specifically, yaw angle refers to the angle at which the vehicle body rotates around its vertical axis (Z-axis). It is essentially the pointing angle of the vehicle. For example, any change in direction will result in a change in yaw angle, such as when turning, changing lanes, or when the vehicle experiences instability such as fishtailing or understeer. While primarily controlled by the steering system and electronic stability program, the suspension system can also assist in influencing yaw response by adjusting the damping force of the left and right wheels, thereby improving cornering agility and stability.

[0066] In some embodiments, vehicle attitude data may also include roll rate, pitch rate, and yaw rate (i.e., the rate of change of the three attitude angles over time), which directly reflect the severity of attitude changes; vehicle height / ground clearance, which reflects the suspension compression and load state; and center of gravity sideslip angle, which characterizes the degree of deviation between the actual driving direction of the vehicle and the direction the vehicle is pointing, and is a key parameter for measuring extreme handling stability.

[0067] In the above embodiments, by specifically defining the composition of vehicle operation data, the key information sources required by the control system are clarified. The aforementioned vehicle posture data collectively constitute a core set of indicators for comprehensively evaluating vehicle ride comfort and handling stability (safety), providing accurate and multi-dimensional input for subsequent accurate calculation of vehicle stability, and ensuring the sufficiency and effectiveness of the control decision-making basis.

[0068] Step 102: Determine the vehicle's operational stability based on vehicle operation data.

[0069] In some embodiments, the vehicle body running stability can be a quantified value of the deviation between the actual motion state of the vehicle body (including vertical acceleration, roll angle, yaw rate, etc.) caused by road surface excitation and driving operation during the vehicle's operation and the vehicle body stability state corresponding to uniform straight-line driving on an ideal flat road surface (i.e., the ideal state with zero vertical acceleration, zero roll angle, and zero yaw rate).

[0070] In some embodiments, the vehicle body stability can be determined based on vehicle operating data by calculating the RMS (root mean square value), which reflects the stability of the vehicle body under the current damping control parameters.

[0071] In some embodiments, the vehicle's stability is determined based on vehicle operating data. This can be achieved by calculating the following parameters as measures of vehicle stability: Root Mean Square Ratio (RMSR), which is the ratio of the RMS of the controlled signal to the target signal, directly measuring the degree of vibration suppression; or Peak Attenuation Rate, calculated as (peak value before control - peak value after control) / peak value before control × 100%, assessing the ability to suppress extreme impacts; or Weighted Root Mean Square Acceleration, highlighting the human-sensitive frequency band (e.g., 0.5-80Hz) through a frequency weighting function, comprehensively evaluating ride comfort; or Standard Deviation of Roll / Pitch Angles, reflecting the degree of fluctuation in vehicle attitude, directly related to handling stability; or Time Domain Error Integration Indices, such as Integral Absolute Error (IAE) or Integral Square Error (ISE), which comprehensively evaluate the control accuracy and transient performance by calculating the integral of the deviation between the vehicle's vertical acceleration or attitude angle and the ideal reference value. These indicators can jointly judge the parameter tuning effect from multiple dimensions, including vibration reduction, comfort, attitude stability, and control accuracy.

[0072] In some embodiments, the vehicle body running stability can also be a combination of multiple of the above parameters, which is not limited in this disclosure.

[0073] In some embodiments, determining the vehicle's operational stability based on vehicle operating data can be achieved by collecting operating data under the initial vehicle chassis damping force while the vehicle is under the control of the initial damping control parameters, thereby obtaining the vehicle operating data corresponding to the initial damping control parameters; or by re-obtaining the vehicle operating data under the control of the current damping control parameters after the initial damping control parameters have been adjusted.

[0074] Step 103: Based on the control strategy of damping control parameters and vehicle body running stability, adaptive stability optimization processing is performed on the vehicle body running stability.

[0075] In some embodiments, adaptive stability optimization processing of the vehicle body running stability can be carried out by taking the vehicle stability reflected by the vehicle body running stability as the adjustment target, and adaptively and gradually adjusting the damping control parameters so that the adjusted damping control parameters can better control the vehicle body running stability.

[0076] In some embodiments, under the control of different damping control parameters, the vehicle corresponds to a certain degree of vehicle running stability. By gradually adjusting the damping control parameters based on the control strategy of damping control parameters and vehicle running stability, adaptive stability optimization processing of the vehicle running stability can be achieved.

[0077] In some embodiments, adaptive stability optimization processing for the vehicle body running stability can be achieved by taking the vehicle body running stability as the adjustment target, that is, taking the vehicle body achieving the stability target as the principle, iteratively updating the damping control parameters and the vehicle body running stability control strategy, so that after the iterative update of the strategy, a control strategy that is more in line with the adjustment target can be output to adjust the damping control parameters of the vehicle chassis.

[0078] In some embodiments, adaptive stability optimization can employ a trained reference model that defines the optimal dynamic response (e.g., smooth vehicle body movement) that the vehicle suspension should exhibit. The core objective of the controller is to force the actual vehicle's dynamic output (e.g., vehicle acceleration) to track the output of this reference model as closely as possible by adjusting its own parameters in real time.

[0079] In some embodiments, adaptive stability optimization can employ a self-tuning regulator, forming a real-time "identification-design-control" loop. This involves assuming a parametric model of the vehicle suspension (such as an ARMAX model), and then using online identification techniques such as recursive least squares to estimate model parameters based on real-time input-output data. Subsequently, based on the latest estimated model parameters, a control law (such as minimum variance control or generalized predictive control) is solved online, and the optimal damping force command at the current moment is calculated.

[0080] In some embodiments, adaptive stability optimization can also be based on neural network / deep learning-based adaptive control, leveraging the powerful nonlinear mapping capabilities and data-learning ability of neural networks. It can be used as a direct adaptive controller, where the network weights, i.e., the control parameters, are updated online via backpropagation based on the error signal; or it can be used as an indirect adaptive controller, where the neural network is used to model complex, nonlinear vehicle suspension dynamics online, and this model is used to calculate the control input.

[0081] In some embodiments, the adaptive stability optimization process may use adaptive control methods in related technologies or any future adaptive control method, which is not limited in this disclosure.

[0082] Step 104: Based on the control strategy of damping control parameters and vehicle stability, determine the damping control parameter adjustment scheme to narrow the gap between the vehicle's running stability and the stability target until the stability target is met, and control the adjustment process of the vehicle chassis's damping control parameters so that the vehicle body achieves the stability target.

[0083] In some embodiments, based on the control strategy of damping control parameters and vehicle stability, a damping control parameter adjustment scheme is determined to narrow the gap between the vehicle running stability and the stability target until the stability target is met. This can be achieved by repeatedly executing step 103 so that the gap between the vehicle running stability achieved by the adjusted damping control parameters and the stability target is narrowed to meet the stability target, thereby obtaining the damping control parameter adjustment scheme.

[0084] In some embodiments, the stability target can be that the vehicle body running stability reaches 0.

[0085] In some embodiments, the vehicle body achieves its stability target by adjusting and controlling the damping control parameters so that the actual operating state of the vehicle approaches the ideal operating state infinitely. The ideal operating condition can be a flat road.

[0086] In the above embodiment, with each cycle of "applying control → determining vehicle stability → updating strategy," the damping control parameters are gradually refined, and the resulting control effect (i.e., changes in damping force) more precisely counteracts vehicle vibrations. Ultimately, through this adaptive fine-tuning, the system drives the vehicle's stability to continuously decrease and stabilize near zero. From a vehicle performance perspective, this means that regardless of the road surface conditions, the chassis system can generate just the right amount of damping force in real time, effectively suppressing vehicle body undulations and swaying, thereby achieving and stabilizing ride comfort at a preset optimal level.

[0087] In the above embodiments, by clearly defining the stability of the vehicle body as the adjustment target, a clear and quantifiable optimization direction is provided for the damping force control. This enables the control system to go beyond simple command following and instead focus on continuously reducing the deviation between the actual motion state of the vehicle body and the ideal state, laying a logical foundation for achieving more precise and intelligent suspension control.

[0088] Figure 2 This is a flowchart illustrating another vehicle control method proposed in an embodiment of this disclosure. Based on Figure 1 The embodiment shown, Figure 2 right Figure 1 Step 102 in the text will be further explained, such as Figure 2 As shown, it includes the following steps.

[0089] Step 201: Determine the degree of vehicle body vibration based on vehicle body acceleration, and determine the degree of attitude angle fluctuation based on vehicle body attitude data.

[0090] In some embodiments, determining the degree of vehicle vibration based on vehicle acceleration can be achieved by calculating the RMS value of the vehicle acceleration to obtain the degree of vehicle vibration.

[0091] In some embodiments, determining the degree of attitude angle fluctuation based on vehicle attitude data can be achieved by calculating the RMS value of at least one data point in the vehicle attitude data to obtain at least one degree of attitude angle fluctuation.

[0092] In some embodiments, the degree of vehicle body vibration and attitude angle fluctuation can be calculated by taking the root mean square value of each signal within the calculation time window as a characterization of its fluctuation intensity.

[0093] Step 202: Determine the vehicle's operational stability based on the degree of vehicle vibration and the degree of attitude angle fluctuation.

[0094] In some embodiments, the vehicle running stability is determined based on the vehicle vibration level and the attitude angle fluctuation level, including: determining a first weighting coefficient corresponding to the vehicle vibration level and a second weighting coefficient corresponding to the attitude angle fluctuation level; and calculating the weighted sum of squares of the vehicle vibration level and the attitude angle fluctuation level according to the first weighting coefficient and the second weighting coefficient to obtain the vehicle running stability.

[0095] In some embodiments, the weighted sum of squares of the vehicle body vibration level and attitude angle fluctuation level is calculated based on the first weighting coefficient and the second weighting coefficient, and the vehicle body running stability (E) can be calculated according to the following formula: E = w1·(RMS1)² + w2·(RMS2)² + w3·(RMS3)² where RMS1 is the root mean square value of vehicle acceleration, RMS2 is the root mean square value of roll angle, RMS3 is the root mean square value of yaw angle, and w1, w2, and w3 are their respective weighting coefficients.

[0096] In some embodiments, the first weighting coefficient corresponds to the degree of vehicle vibration corresponding to the vehicle acceleration, and the second weighting coefficient corresponds to the degree of attitude angle fluctuation corresponding to the vehicle attitude data. When performing RMS calculation on multiple vehicle attitude data to obtain the corresponding degree of attitude angle fluctuation, there are also multiple second weighting coefficients.

[0097] In the above embodiments, by determining the weight coefficients used to calculate the vehicle's operational stability based on the real-time vehicle vibration level and attitude angle fluctuation level, the determination of weight parameters based on different driving styles and driving environments is realized, so that the vehicle's operational stability meets the actual needs of the current vehicle, accurately measuring the current vehicle's operating state and improving the adaptability and accuracy of the vehicle control method.

[0098] In some embodiments, determining a first weighting coefficient corresponding to the degree of vehicle body vibration and a second weighting coefficient corresponding to the degree of attitude angle fluctuation includes: determining the vehicle driving mode; and based on the vehicle driving mode, determining a set of weighting coefficients associated with the vehicle driving mode, the set of weighting coefficients including the first weighting coefficient and the second weighting coefficient.

[0099] In some embodiments, the set of weighting coefficients includes the weights corresponding to vehicle body vibration values, roll angle fluctuation values, and yaw angle fluctuation values, etc.

[0100] In some embodiments, the vehicle driving mode may be to identify driving scenarios, such as the controller determining the current driving scenario in real time based on cameras, radar, navigation maps and vehicle operation data.

[0101] In some embodiments, there is a corresponding correlation between the vehicle driving mode and the set of weight coefficients. For example, there are preset scenarios and their weight preferences. Scenario A is long-distance cruising (recognition conditions: vehicle speed > 80km / h and stable, clear lane lines, no vehicles ahead), so the weight preference is extremely high comfort (smoothness) and moderate directional stability. Scenario B is urban congestion (recognition conditions: vehicle speed < 40km / h and frequent starts and stops), so the weight preference is suppressing nose-dive / nose-up (related to smoothness), and the requirements for roll and yaw stability can be reduced. Scenario C is mountain road curves (recognition conditions: high lateral acceleration, large and frequent steering wheel angles), so the weight preference is extremely high roll and yaw stability (handling), and smoothness can be appropriately sacrificed. Scenario D is bumpy road conditions (recognition conditions: continuous high frequency and high amplitude vertical acceleration), so the weight preference is extremely high smoothness, while maintaining vehicle stability.

[0102] In some embodiments, the set of weighting coefficients determined based on the vehicle driving mode can be obtained by the driver selecting a "Comfort," "Sport," or "Auto" mode via a button. This selection makes a final adjustment to the weights. For example, in "Comfort" mode, the weights related to ride comfort (i.e., w1) are amplified in all scenarios. In "Sport" mode, the weights related to stability (i.e., w2, w3) are amplified in all scenarios. In "Auto" mode, the weights corresponding to the pre-defined relationships are used entirely. For example, in vehicle chassis adaptive control, the input is set to real-time acquisition of vehicle acceleration and attitude data measured by vehicle sensors; based on the vehicle acceleration and attitude data, the error is calculated as the deviation between the vehicle state and the desired target.

[0103] In the above embodiments, by acquiring the vehicle's driving mode, the corresponding weight coefficient is determined, so that the weight coefficient matches the current vehicle's driving mode, improving the vehicle's stability and accurately reflecting the vehicle's operating status, thereby further improving the adaptability and accuracy of the vehicle control method.

[0104] In the above embodiments, a structured method for constructing vehicle running stability is provided by separately determining the two physically meaningful sub-items, "vehicle vibration level" (primarily related to comfort) and "attitude angle fluctuation level" (primarily related to stability), and then combining them. This not only makes the calculation process of vehicle running stability more transparent and adjustable, but also facilitates the flexible and targeted balancing of vehicle comfort and handling requirements under different operating conditions by adjusting the weight coefficients of the two sub-items separately.

[0105] Figure 3 This is a flowchart illustrating another vehicle control method proposed in an embodiment of this disclosure. Based on Figure 1-2 The embodiment shown, Figure 3 right Figure 1Step 103 in the text will be further explained, such as Figure 3 As shown, it includes the following steps.

[0106] Step 301: Based on the vehicle body's running stability and the vehicle chassis's vertical acceleration, the damping control parameters to be adjusted are determined through adaptive filtering.

[0107] In some embodiments, the damping control parameter to be adjusted may be the solenoid valve current value, which is used to adjust the initial damping force to the first damping force.

[0108] In some embodiments, the vehicle body running stability and the vehicle chassis vertical acceleration are used as inputs for adaptive filtering. By determining the damping control parameters to be adjusted, the vehicle is controlled under different chassis damping forces so that the vehicle body running stability gradually changes towards a stable direction.

[0109] In some embodiments, based on the vehicle body's running stability and the vehicle chassis's vertical acceleration, adaptive filtering is used to determine the damping control parameters to be adjusted, including: determining the filter weights based on the vehicle body's running stability, the vehicle chassis's vertical acceleration, and preset coefficients; and determining the damping control parameters to be adjusted based on the vehicle chassis's vertical acceleration and the filter weights.

[0110] In some embodiments, based on the vehicle body running stability, the vehicle chassis vertical acceleration, and a preset coefficient, the filtering weights of the damping control parameters and the vehicle body running stability control strategy can be determined. Based on these filtering weights and the real-time vehicle chassis vertical acceleration, the damping control parameters to be adjusted for adjusting the vehicle chassis damping force can be obtained.

[0111] In some embodiments, adaptive filtering can adaptively adjust the filter weights based on the vehicle chassis vertical acceleration and the vehicle's running stability, so that the damping control parameters to be adjusted in each output can control the vehicle chassis damping force, thereby adjusting the vehicle's running stability in the direction of stability.

[0112] In some embodiments, the filter weights are the parameters to be updated for each adaptive filtering process of the filter. After each output of the damping control parameters to be adjusted, the chassis damping force of the vehicle is adjusted, resulting in a new vehicle running stability. This vehicle running stability is used as a new input to the filter and, together with the updated filter weights, generates new chassis control parameters to be adjusted, thereby adjusting the chassis damping force of the vehicle and achieving adaptive stability optimization processing of the vehicle running stability.

[0113] In some embodiments, the filtering weights are determined based on the vehicle body running stability, the vehicle chassis vertical acceleration, and a preset coefficient, including: determining the initial filtering weights; and adding the product of the vehicle body running stability, the calculated value of the vehicle chassis vertical acceleration, and the preset coefficients with the initial filtering weights to determine the filtering weights.

[0114] In some embodiments, the initial filter weights can be the initial weight parameters of the adaptive filter, serving as the baseline values ​​for the first adaptive filtering process.

[0115] In some embodiments, the initial filter weights are the baseline values ​​for each adaptive filtering process. In other words, after the filter weights are updated, the updated filter weights are used as the initial filter weights for the next adaptive filtering process.

[0116] In some embodiments, the filtering weights are determined based on the vehicle's running stability, the vehicle chassis vertical acceleration, and a preset coefficient. This can be achieved by updating the initial filtering weights based on the vehicle's running stability, the preset coefficient, and the vehicle chassis vertical acceleration. Specifically, the vehicle's running stability can be convolved with the vehicle chassis vertical acceleration after secondary path filtering, and the product of the preset coefficient and the convolution can be used as the increment value of the filtering weights. This increment value is then added to the initial filtering weights to obtain the filtering weights for the current processing step.

[0117] In some embodiments, the calculated value of the vehicle chassis vertical acceleration can be obtained by using the vehicle chassis vertical acceleration as input to a secondary filter and estimating it through the transfer function of the secondary filter.

[0118] In some embodiments, the estimated value of the vehicle chassis vertical acceleration is multiplied by the vehicle body running stability and a preset coefficient, and then added to the initial filter weights to determine the filter weights.

[0119] In some embodiments, the preset coefficient may be a pre-set step size coefficient. In different embodiments, it may be customized according to needs or scenarios, and this disclosure does not limit it.

[0120] In some embodiments, adaptive filtering can estimate the vertical acceleration of the vehicle chassis through secondary filtering to obtain the estimated vertical acceleration of the vehicle chassis, which is then used as a parameter to update the filter weights.

[0121] For example, the formula for updating the filter weights is as follows: ; Where w(n) is the weight to be updated, w(n+1) is the updated weight, and μ is the step size coefficient; e(n) is the RMS calculated from the vehicle body posture data; It is an estimate of the input signal after secondary path filtering, which is the convolution of the transfer function of the secondary path filter and the input signal. The input signal is the vertical acceleration of the vehicle chassis. ; Where X(n) is the vertical acceleration of the vehicle chassis; It is the transfer function of the secondary path filter.

[0122] In some embodiments, the transfer function of the secondary path filter can be preset, and the vertical acceleration of the vehicle chassis can be used as the output of the secondary path filter. After processing by the transfer function, the estimated value of the vertical acceleration of the vehicle chassis can be obtained.

[0123] For example, the weighting process of the adaptive filter is as follows: Establish a secondary path model; establish a transfer function model from the change in solenoid valve current to the vehicle acceleration / attitude response (which can be obtained based on vehicle dynamics and the solenoid valve model); design an adaptive filter controller, which can be designed as a multi-order FIR filter, where the weights are the adjustment parameters, the input is road disturbance (or its estimate), and the output is the current adjustment signal; update driven by the error signal; feedback error (vehicle acceleration + attitude error) serves as a performance indicator, driving the online update of the filter weights.

[0124] In some embodiments, determining the damping control parameters to be adjusted based on the vehicle chassis vertical acceleration and filter weights includes: performing a convolution operation between the vehicle chassis vertical acceleration and the filter weights to determine the damping control parameters to be adjusted.

[0125] In some embodiments, the damping control parameters to be adjusted are determined based on the vehicle chassis vertical acceleration and the filter weights. This can be achieved by using the vehicle chassis vertical acceleration as the input of the adaptive filter and convolving it with the filter weights to obtain the damping control parameters to be adjusted, namely the current value of the first solenoid valve.

[0126] For example, the input signal x(n) is the road surface excitation, i.e., the chassis acceleration signal (the vertical acceleration of the vehicle chassis); the control signal y(n) = w T (n)x(n): The current regulation of the solenoid valve, and w(n) is the weight vector. Here, both w(n) and x(n) are vectors, and y(n) is the product of the transpose of w(n) and x(n), or the product of the transpose of x(n) and w(n). n represents the current time.

[0127] In some embodiments, after each adaptive filtering process, the vehicle's running stability is adjusted and the filter weights are updated. The next adaptive filtering process is then performed based on the adjusted vehicle running stability and the updated filter weights to obtain new damping control parameters to be adjusted. This adjusts the vehicle chassis damping force, thereby optimizing the vehicle's running stability.

[0128] Step 302: Adjust the initial damping control parameters based on the damping control parameters to be adjusted, and determine the vehicle body running stability under the adjusted vehicle chassis damping force.

[0129] In some embodiments, after adjusting the vehicle chassis damping force based on the damping control parameters to be adjusted, the vehicle body running data under the adjusted vehicle chassis damping force is obtained, and the adjusted vehicle body running stability is determined based on the adjusted vehicle running data. Adaptive stability optimization processing is then performed on the adjusted vehicle body running stability based on the control strategy of damping control parameters and vehicle body running stability.

[0130] In some embodiments, after obtaining the first solenoid valve current value, the first solenoid valve current value is sent to the execution module to control the opening of the solenoid valve, thereby adjusting the vehicle chassis damping force. Under the new vehicle chassis damping force, the vehicle body running state under the current vehicle chassis damping force is collected, and the vehicle body running stability is calculated.

[0131] In some embodiments, the specific implementation method for determining the new vehicle body running stability based on the vehicle body running data under the new vehicle chassis damping force can be found in [reference needed]. Figure 2 The specific implementation methods in the illustrated embodiments will not be described in detail here.

[0132] In the above embodiments, a closed-loop logic of "prediction-verification-adjustment" is established: First, a control action (damping control parameters to be adjusted) is given based on initial parameters and executed; then, the actual effect after execution (vehicle stability) is detected, and optimization is performed based on the actual effect, i.e., the filter weights are updated to redetermine the vehicle stability based on the new vehicle operating state, thereby achieving optimization of the vehicle stability. This mechanism ensures that the control system does not operate blindly; only when the current strategy fails to achieve the expected results will the parameter optimization process, which consumes more computational resources, be initiated, thus improving the system's operating efficiency and reliability.

[0133] Figure 4 This is a flowchart illustrating another vehicle control method proposed in an embodiment of this disclosure. Based on Figures 1-3 The embodiment shown, Figure 4 right Figure 1Step 104 in the text will be further explained, such as Figure 4 As shown, it includes the following steps.

[0134] Step 401: In response to the discrepancy between the redefined vehicle body running stability and the stability target not meeting the stability target, the damping control parameters to be adjusted are redefined based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, through adaptive filtering.

[0135] In some embodiments, the stability target can be a pre-defined ideal state for the vehicle to drive on a stable road surface or a smooth road, i.e., the vehicle's stability tends to be 0. Specifically, the higher the value of the vehicle's running stability, the stronger the energy of the vehicle's vibration and the worse the vehicle's stability. Conversely, the lower the value of the vehicle's running stability, the smoother the vehicle's vibration and the better the stability. By using the difference between the vehicle's running stability and the stability target as a marker to measure whether the adaptive stability optimization process has ended, adaptive optimization control of the vehicle's chassis damping force can be achieved.

[0136] In some embodiments, the adaptive stability optimization process for improving the vehicle's operational stability, based on the control strategy of damping control parameters and vehicle body running stability, can be as follows: Figure 3 The specific implementation of the illustrated embodiment will not be repeated here. By repeatedly executing the adaptive stability optimization process, after each adjustment, a new vehicle body running stability is obtained. The difference between this vehicle body running stability and the stability target is calculated. When the vehicle body running stability approaches the stability target but the difference is still large, it indicates that the current result does not meet the stability target. It is necessary to perform the adaptive stability optimization process on the vehicle body running stability again. That is, based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, the damping control parameters to be adjusted are re-determined through adaptive filtering.

[0137] In some embodiments, based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, the damping control parameters to be adjusted are re-determined through adaptive filtering. This can be achieved by updating the damping control parameters and the control strategy for vehicle body running stability based on the vehicle body running stability, the vehicle chassis vertical acceleration, and preset coefficients. Using the updated control strategy, the damping control parameters to be adjusted for adjusting the vehicle chassis damping force can be obtained.

[0138] For example, after obtaining the first solenoid valve current value, the opening of the solenoid valve is controlled based on this value. The damping force of the vehicle chassis is then adjusted from the initial damping force to the first damping force. Under this first damping force, vehicle operation data is acquired, and a first level of vehicle body stability is determined based on this data. It is then determined whether the gap between the first level of vehicle body stability and the stability target has narrowed to meet the stability target. If not, adaptive filtering is performed based on the first level of vehicle body stability and the vertical acceleration of the vehicle chassis under the first damping force to obtain the second solenoid valve current value. During this adaptive filtering process, the filter weights are updated; that is, the second solenoid valve current value is determined based on the updated filter weights and the vertical acceleration of the vehicle chassis under the first damping force.

[0139] Step 402: Based on the newly determined damping control parameters to be adjusted, adjust the damping force of the vehicle chassis on which the vehicle is located until the gap between the vehicle body running stability and the stability target is narrowed to meet the stability target, and determine the damping control parameter adjustment scheme.

[0140] In some embodiments, the damping force of the vehicle chassis is adjusted based on the redefined damping control parameters to be adjusted. Specifically, a control command is generated based on the redefined damping control parameters to adjust the damping force of the vehicle chassis. Specifically, a control command is generated based on the current value of the second solenoid valve to control the opening of the solenoid valve, thereby adjusting the damping force of the vehicle chassis and obtaining the second damping force.

[0141] In some embodiments, for the second solenoid valve current value, it is necessary to determine whether the vehicle's body stability under the control of this current value reaches the stability target. If it does, the second solenoid valve current value is the final result of adaptive control; otherwise, if the stability target is not reached, the next round of filter weight update needs to be performed based on the vehicle's body stability, that is, the process is executed again. Figure 2 In the embodiment shown, the vehicle's operational stability is maintained until the solenoid valve current value obtained after multiple filter weight updates reaches the stability target.

[0142] In some embodiments, after multiple rounds of adaptive stability optimization processing of the vehicle's running stability through a control strategy based on damping control parameters and vehicle running stability, once the gap between the vehicle's running stability and the stability target is narrowed to the point where the stability target is met, the damping control parameter adjustment scheme is determined to be to adjust the current damping control parameters to the newly determined damping control parameters to be adjusted.

[0143] In the above embodiments, the control strategy is revised by utilizing the previous control effect (updating the filter weights), and a better control command is generated based on the revised strategy and the new input (vehicle chassis vertical acceleration). Through this continuous "evaluation-correction-execution" cycle, the system can gradually reduce the vehicle's stability until the preset convergence condition is met, thereby achieving online self-learning and performance self-optimization of the control strategy.

[0144] In summary, the vehicle control method proposed in this disclosure provides a closed-loop control scheme for vehicle damping force based on multi-source sensing and adaptive filtering. By fusing multi-dimensional dynamic data such as vehicle acceleration and attitude angles in real time, a comprehensive state error, i.e., the vehicle's operational stability, is constructed that balances comfort and stability. Using this as the target, an adaptive filtering process, including secondary path compensation, is used to iteratively optimize control parameters online. This method achieves end-to-end intelligent decision-making from road excitation perception and vehicle state assessment to precise damping force adjustment, enabling the suspension system to actively and accurately counteract vibrations. Without requiring a precise vehicle model, it dynamically balances ride comfort and handling stability, improving the vehicle's adaptability to different road conditions and loads.

[0145] The following is a specific implementation of a vehicle chassis adaptive control proposed in this disclosure.

[0146] Figure 5 This is a flowchart illustrating a vehicle chassis adaptive control system.

[0147] I. Set control objectives.

[0148] Reduce vehicle body acceleration error (vertical vibration, improving comfort).

[0149] Control vehicle body posture errors (such as roll angle, to improve handling stability).

[0150] The input and output definitions are shown in Table 1.

[0151] Table 1: Parameter Definition Table

[0152] II. Adaptive control.

[0153] In active noise reduction, the FxLMS (Filtered-x LMS) algorithm primarily aims to adjust the input signal through an adaptive filter to cancel noise. Its core principle is to use the error signal to drive weight updates and achieve real-time adaptation. The corresponding mapping relationship is shown in Table 2.

[0154] Table 2: Correspondence Mapping Relationship Table

[0155] 1. Brief description of the process.

[0156] Measurement error: Real-time calculation of vehicle body state error (weighted sum of acceleration and attitude errors).

[0157] Filtered input: The control signal is filtered through the secondary path model to obtain a prediction of the impact on the error, which is used to calculate the gradient.

[0158] Weight update: Based on the error and the filtered control signal, the LMS algorithm is used to iteratively update the controller parameters (which can be understood as a current regulation strategy).

[0159] Output control: The updated control signal is applied to the solenoid valve to adjust the damping force.

[0160] 2. Implementation process.

[0161] (1) Establish a secondary path model; (2) Establish a transfer function model from the change of solenoid valve current to the vehicle body acceleration / attitude response (which can be obtained based on vehicle dynamics and solenoid valve model). (3) Design an adaptive filter; (4) The controller can be designed as a multi-stage FIR filter, with the weights being the adjustment parameters, the input being the road disturbance (or its estimate), and the output being the current adjustment signal; (5) Error signal-driven update; (6) Feedback error (vehicle acceleration + attitude error) is used as a performance indicator to drive the online update of filter weights.

[0162] III. Specific representation of input and output.

[0163] Input signal x(n): Road surface excitation, i.e., chassis acceleration signal; Error signal e(n): Real-time measured vehicle body attitude error; Control signal y(n)=w T (n)x(n): Solenoid valve current regulation amount, w(n) is the weight vector; Weight update: , in It is the input signal after secondary path filtering, and μ is the step size coefficient.

[0164] In summary, the above solution can take the real-time posture comfort of the actual vehicle as the optimization target, and achieve high-precision real-time chassis control through a combination of feedforward and feedback, resulting in better comfort and handling experience. It overturns the motor control paradigm that uses parameter calibration as the motor control logic and realizes the innovation of intelligent chassis technology.

[0165] Figure 6This is a schematic diagram of the structure of a vehicle control device 600 according to an embodiment of this disclosure. Figure 6 As shown, the vehicle control device includes: a data acquisition module 610, a determination module 620, a processing module 630, and a control module 640.

[0166] The acquisition module is used to acquire vehicle operation data, which is the vehicle's operation data under the damping force of the vehicle chassis. The operation data reflects the vehicle's body operation status under the damping force of the vehicle chassis. The determination module is used to determine the vehicle's operational stability based on vehicle operating data; The processing module is used to perform adaptive stability optimization processing on the vehicle's running stability based on the control strategy of damping control parameters and vehicle running stability. The control module is used to determine the damping control parameter adjustment scheme based on the control strategy of damping control parameters and vehicle body running stability, so as to narrow the gap between the vehicle body running stability and the stability target until the stability target is met, and to control the adjustment process of the vehicle chassis damping control parameters so that the vehicle body achieves the stability target.

[0167] In some embodiments, vehicle operating data includes vehicle body acceleration, vehicle body attitude data, and vehicle chassis vertical acceleration; vehicle body attitude data includes at least one of the following: roll angle; yaw angle; pitch angle.

[0168] In some embodiments, the determining module is used to: determine the degree of vehicle vibration based on vehicle acceleration, and determine the degree of attitude angle fluctuation based on vehicle attitude data; and determine the degree of vehicle running stability based on the degree of vehicle vibration and the degree of attitude angle fluctuation.

[0169] In some embodiments, the determining module is used to: determine a first weighting coefficient corresponding to the degree of vehicle vibration and a second weighting coefficient corresponding to the degree of attitude angle fluctuation; and calculate the weighted sum of squares of the degree of vehicle vibration and the degree of attitude angle fluctuation based on the first weighting coefficient and the second weighting coefficient to obtain the degree of vehicle running stability.

[0170] In some embodiments, the determining module is configured to: determine the vehicle driving mode; and based on the vehicle driving mode, determine a set of weight coefficients associated with the vehicle driving mode, the set of weight coefficients including a first weight coefficient and a second weight coefficient.

[0171] In some embodiments, the processing module is configured to: determine the damping control parameters to be adjusted based on the vehicle body running stability and the vehicle chassis vertical acceleration through adaptive filtering; adjust the initial damping control parameters based on the damping control parameters to be adjusted; and determine the vehicle body running stability under the adjusted vehicle chassis damping force.

[0172] In some embodiments, the processing module is used to: determine the filter weights based on the vehicle body running stability, the vehicle chassis vertical acceleration, and a preset coefficient; and determine the damping control parameters to be adjusted based on the vehicle chassis vertical acceleration and the filter weights.

[0173] In some embodiments, the processing module is used to: determine the initial filter weights; and perform an addition operation on the product of the vehicle body running stability, the calculated value of the vehicle chassis vertical acceleration, and the preset coefficients with the initial filter weights to determine the filter weights.

[0174] In some embodiments, the processing module is configured to: perform a convolution operation on the vertical acceleration of the vehicle chassis and the filter weights to determine the damping control parameters to be adjusted.

[0175] In some embodiments, the control module is configured to: in response to the fact that the gap between the redefined vehicle body running stability and the stability target does not meet the stability target, based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, re-determine the damping control parameters to be adjusted through adaptive filtering; based on the re-determined damping control parameters to be adjusted, adjust the damping force of the vehicle chassis on which the vehicle is located until the gap between the vehicle body running stability and the stability target is reduced to meet the stability target, and determine the damping control parameter adjustment scheme.

[0176] In summary, the vehicle control device proposed in this disclosure can achieve precise adjustment of damping force based on road excitation perception and vehicle body state assessment, enabling the suspension system to actively and accurately counteract vibrations. During real-time vehicle operation, it dynamically balances driving comfort and handling stability, thereby improving the vehicle's adaptability to different road conditions and loads.

[0177] The vehicle control device proposed in this disclosure has broad platform adaptability and can be deployed in vehicle domain controllers or embedded ECUs of pure electric, hybrid and fuel-powered passenger vehicles, commercial vehicles and special vehicles. It also supports operation on edge computing units, cloud service platforms and mobile terminals. It is compatible with automotive-grade operating systems such as Linux and QNX and mainstream simulation development environments. It can achieve vehicle-cloud collaborative parameter tuning through CAN, Ethernet and 4G / 5G networks, and is suitable for all scenarios from real-time optimization of single vehicles to intelligent fleet management in the cloud.

[0178] In some embodiments, the vehicle control device may be applicable to vehicle types including: power type, such as pure electric, hybrid, fuel cell and gasoline vehicles; vehicle form, such as passenger car, commercial vehicle, special operation vehicle and autonomous vehicle, etc.

[0179] In some embodiments, the vehicle control device may be applied to a processing platform such as an in-vehicle computing platform or an edge computing unit.

[0180] In some embodiments, the vehicle control device may be applicable to server-side and cloud systems, such as cloud service platforms or data analysis platforms.

[0181] In some embodiments, the vehicle control device may be applicable electronic devices and hardware, such as mobile terminals, Internet of Things devices, etc.

[0182] In some embodiments, the computer system to which the vehicle control device is applicable can be any operating system, development environment, simulation system, etc.

[0183] Figure 7 This is a block diagram illustrating a vehicle 700 according to an exemplary embodiment. For example, vehicle 700 can be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 700 can be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0184] Reference Figure 7 The vehicle 700 may include various subsystems, such as an infotainment system 710, a perception system 720, a decision control system 730, a drive system 740, and a computing platform 750. The vehicle 700 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of the vehicle 700 can be interconnected via wired or wireless means.

[0185] In some embodiments, the infotainment system 710 may include a communication system, an entertainment system, and a navigation system, etc.

[0186] The perception system 720 may include several sensors for sensing information about the environment surrounding the vehicle 700. For example, the perception system 720 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0187] The decision control system 730 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0188] The drive system 740 may include components that provide powered motion to the vehicle 700. In one embodiment, the drive system 740 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0189] Some or all of the functions of vehicle 700 are controlled by computing platform 750. Computing platform 750 may include at least one processor 751 and memory 752, and processor 751 may execute instructions 753 stored in memory 752.

[0190] Processor 751 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0191] The memory 752 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0192] In addition to instruction 753, memory 752 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 752 can be used by computing platform 750.

[0193] In this embodiment of the disclosure, the processor 751 may execute instructions 753 to complete all or part of the steps of the vehicle control method described above.

[0194] Embodiments of this disclosure also provide a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the vehicle control method described in the above embodiments of this disclosure.

[0195] Embodiments of this disclosure also provide a computer program product, including a computer program that is executed by a processor using the vehicle control method described in the above embodiments of this disclosure.

[0196] Embodiments of this disclosure also propose a vehicle that executes the vehicle control method proposed in this disclosure to control the damping force of the vehicle chassis.

[0197] In some embodiments, the electronic device is configured to perform the vehicle control method described above, or the electronic device includes the vehicle control apparatus described above. The electronic device may be a vehicle, a cloud server, or a terminal device or cloud device used solely for performing the vehicle control method, etc., which are not limited in this disclosure.

[0198] Figure 8 This is a schematic diagram illustrating the structure of a chip 800 for implementing the above-described vehicle control method, according to an exemplary embodiment. (Refer to...) Figure 8 The chip 800 includes at least one communication interface 801 and a processor 802. The communication interface 801 is used to receive signals input to the chip 800 or signals output from the chip 800. The processor 802 communicates with the communication interface 801 and implements the vehicle control method described in the above embodiments of this disclosure through logic circuits or executing code instructions.

[0199] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0200] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this disclosure. 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.

[0201] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure 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 function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0202] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (control method), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0203] It should be understood that various parts of the embodiments of this disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in 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 one or a combination of the following techniques known in the art: 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 (PGAs), field-programmable gate arrays (FPGAs), etc.

[0204] Those skilled in the art will understand that all or part of the steps of the methods in 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.

[0205] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a single processing module, or each unit can exist physically separately, or two or more units can be integrated into a single module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The aforementioned storage medium can be a read-only memory, a hard disk, or an optical disk, etc.

[0206] Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A vehicle control method, characterized in that, The method includes: Acquire vehicle operation data, which is the vehicle's operation data under the damping force of the vehicle chassis, and the operation data reflects the vehicle's body operation state under the damping force of the vehicle chassis. Based on the vehicle operation data, the vehicle body's operational stability is determined; Based on the control strategy of damping control parameters and vehicle running stability, adaptive stability optimization processing is performed on the vehicle running stability. Based on the control strategy of the damping control parameters and the vehicle body running stability, a damping control parameter adjustment scheme is determined to narrow the gap between the vehicle body running stability and the stability target until the stability target is met. The adjustment process of the damping control parameters of the vehicle chassis is controlled so that the vehicle body achieves the stability target.

2. The method according to claim 1, characterized in that, The vehicle operation data includes vehicle body acceleration, vehicle body attitude data, and vehicle chassis vertical acceleration; The vehicle attitude data includes at least one of the following: roll angle; yaw angle; pitch angle.

3. The method according to claim 2, characterized in that, Based on the vehicle operation data, the vehicle body's operational stability is determined, including: The degree of vehicle vibration is determined based on the vehicle acceleration, and the degree of attitude angle fluctuation is determined based on the vehicle attitude data; The vehicle's operational stability is determined based on the degree of vehicle vibration and the degree of attitude angle fluctuation.

4. The method according to claim 3, characterized in that, The determination of the vehicle's operational stability based on the vehicle body vibration level and the attitude angle fluctuation level includes: Determine a first weighting coefficient corresponding to the degree of vehicle body vibration, and a second weighting coefficient corresponding to the degree of attitude angle fluctuation; The vehicle body vibration level and the attitude angle fluctuation level are weighted and squared to calculate the vehicle body running stability based on the first weighting coefficient and the second weighting coefficient.

5. The method according to claim 4, characterized in that, The determination of the first weighting coefficient corresponding to the degree of vehicle body vibration and the second weighting coefficient corresponding to the degree of attitude angle fluctuation includes: Determine the vehicle's driving mode; Based on the vehicle driving mode, a set of weight coefficients associated with the vehicle driving mode is determined, the set of weight coefficients including the first weight coefficient and the second weight coefficient.

6. The method according to any one of claims 1 to 5, characterized in that, The control strategy based on damping control parameters and vehicle body running stability performs adaptive stability optimization processing on the vehicle body running stability, including: Based on the vehicle body's operational stability and the vehicle chassis's vertical acceleration, the damping control parameters to be adjusted are determined through adaptive filtering. The initial damping control parameters are adjusted based on the damping control parameters to be adjusted, and the vehicle body running stability under the adjusted vehicle chassis damping force is determined.

7. The method according to claim 6, characterized in that, Based on the vehicle body's operational stability and the vehicle chassis's vertical acceleration, the damping control parameters to be adjusted are determined through adaptive filtering, including: The filtering weights are determined based on the vehicle body's operational stability, the vehicle chassis's vertical acceleration, and preset coefficients. The damping control parameters to be adjusted are determined based on the vehicle chassis vertical acceleration and the filter weights.

8. The method according to claim 7, characterized in that, The step of determining the filter weights based on the vehicle body's operational stability, the vehicle chassis's vertical acceleration, and preset coefficients includes: Determine the initial filter weights; The filter weight is determined by adding the product of the vehicle body running stability, the calculated value of the vehicle chassis vertical acceleration, and the preset coefficient with the initial filter weight.

9. The method according to claim 7, characterized in that, The step of determining the damping control parameters to be adjusted based on the vehicle chassis vertical acceleration and the filter weights includes: The vertical acceleration of the vehicle chassis is convolved with the filter weights to determine the damping control parameters to be adjusted.

10. The method according to claim 9, characterized in that, Based on the control strategy of the damping control parameters and vehicle body running stability, a damping control parameter adjustment scheme is determined to narrow the gap between the vehicle body running stability and the stability target until the stability target is met. The process of adjusting the damping control parameters of the vehicle chassis is controlled to ensure that the vehicle body achieves the stability target, including: In response to the discrepancy between the redefined vehicle body running stability and the stability target not meeting the stability target, the damping control parameters to be adjusted are redefined based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, through the adaptive filtering process. Based on the redefined damping control parameters to be adjusted, the damping force of the vehicle chassis is adjusted until the gap between the vehicle's running stability and the stability target is reduced to meet the stability target, and the damping control parameter adjustment scheme is determined.

11. A vehicle control device, characterized in that, The device includes: a data acquisition module, a determination module, a processing module, and a control module. The acquisition module is used to acquire vehicle operation data, which is the vehicle's operation data under the damping force of the vehicle chassis. The operation data reflects the vehicle's body operation state under the damping force of the vehicle chassis. The determining module is used to determine the vehicle's operational stability based on the vehicle's operating data; The processing module is used to perform adaptive stability optimization processing on the vehicle's running stability based on the control strategy of damping control parameters and vehicle running stability. The control module is used to determine a damping control parameter adjustment scheme based on the control strategy of the damping control parameters and the vehicle body running stability, so as to reduce the gap between the vehicle body running stability and the stability target until the stability target is met, and to control the adjustment process of the damping control parameters of the vehicle chassis so that the vehicle body achieves the stability target.

12. The vehicle control device according to claim 11, characterized in that, The vehicle operation data includes vehicle body acceleration, vehicle body attitude data, and vehicle chassis vertical acceleration; The vehicle attitude data includes at least one of the following: roll angle; yaw angle; pitch angle.

13. The vehicle control device according to claim 12, characterized in that, The determining module is used for: The degree of vehicle vibration is determined based on the vehicle acceleration, and the degree of attitude angle fluctuation is determined based on the vehicle attitude data; The vehicle's operational stability is determined based on the degree of vehicle vibration and the degree of attitude angle fluctuation.

14. The vehicle control device according to claim 13, characterized in that, The determining module is used for: Determine a first weighting coefficient corresponding to the degree of vehicle body vibration, and a second weighting coefficient corresponding to the degree of attitude angle fluctuation; The vehicle body vibration level and the attitude angle fluctuation level are weighted and squared to calculate the vehicle body running stability based on the first weighting coefficient and the second weighting coefficient.

15. The vehicle control device according to claim 14, characterized in that, The determining module is used for: Determine the vehicle's driving mode; Based on the vehicle driving mode, a set of weight coefficients associated with the vehicle driving mode is determined, the set of weight coefficients including the first weight coefficient and the second weight coefficient.

16. The vehicle control device according to any one of claims 11 to 15, characterized in that, The processing module is used for: Based on the vehicle body's operational stability and the vehicle chassis's vertical acceleration, the damping control parameters to be adjusted are determined through adaptive filtering. The initial damping control parameters are adjusted based on the damping control parameters to be adjusted, and the vehicle body running stability under the adjusted vehicle chassis damping force is determined.

17. The vehicle control device according to claim 16, characterized in that, The processing module is used for: The filtering weights are determined based on the vehicle body's operational stability, the vehicle chassis's vertical acceleration, and preset coefficients. The damping control parameters to be adjusted are determined based on the vehicle chassis vertical acceleration and the filter weights.

18. The vehicle control device according to claim 17, characterized in that, The processing module is used for: Determine the initial filter weights; The filter weight is determined by adding the product of the vehicle body running stability, the calculated value of the vehicle chassis vertical acceleration, and the preset coefficient with the initial filter weight.

19. The vehicle control device according to claim 18, characterized in that, The processing module is used for: The vertical acceleration of the vehicle chassis is convolved with the filter weights to determine the damping control parameters to be adjusted.

20. The vehicle control device according to claim 19, characterized in that, The control module is used for: In response to the discrepancy between the redefined vehicle body running stability and the stability target not meeting the stability target, the damping control parameters to be adjusted are redefined based on the vehicle body running stability under the adjusted vehicle chassis damping force and the vehicle chassis vertical acceleration under the adjusted vehicle chassis damping force, through the adaptive filtering process. Based on the redefined damping control parameters to be adjusted, the damping force of the vehicle chassis is adjusted until the gap between the vehicle's running stability and the stability target is reduced to meet the stability target, and the damping control parameter adjustment scheme is determined.

21. A vehicle, characterized in that, The vehicle is configured to perform the vehicle control method of any one of claims 1 to 10, or the vehicle includes the vehicle control device of any one of claims 11 to 20.

22. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 10.

23. A program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 1 to 10.

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