Multi-mode direction self-adaptive control method for solenoid valve of shock absorber

Abstract

The invention discloses a multi-mode direction self-adaptive control method for an electromagnetic valve of a shock absorber, and belongs to the field of vehicle shock absorption control. According to the method, multi-mode signals such as vehicle body acceleration and suspension displacement are collected and preprocessed, working condition modes are divided based on the vehicle speed, the vibration dominant frequency and the roll angle, the damping direction opening ratio of the compression stage and the rebound stage is set according to different working conditions, parameters are dynamically adjusted in combination with the vibration amplitude and frequency, and a control signal is generated to drive an electromagnetic valve to act; meanwhile, a low-power-consumption mode triggered by a vibration energy threshold value and an energy recovery function are achieved, and driver habit self-learning and virtual estimation when a sensor breaks down are achieved through machine learning. According to the method, self-adaptive adjustment of the damping force under the complex working condition is achieved, vehicle driving smoothness and control stability are improved, system energy consumption is reduced, and system adaptability and reliability are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of vehicle shock absorption control, and in particular to a multi-modal direction adaptive control method for a shock absorber solenoid valve. Background Art

[0002] In the current field of vehicle shock absorber control technology, traditional solenoid valve control methods mostly use a single fixed parameter strategy, responding to road excitation through preset damping characteristics. In terms of technical implementation, they mostly rely on single or small amounts of sensor data such as vehicle acceleration and wheel speed, and lack the integrated utilization of multimodal data such as vehicle acceleration, suspension displacement, and steering angle. In terms of operating condition identification, there is no precise division mechanism based on multi-dimensional parameters such as vehicle speed, main vibration frequency, and roll angle. Damping force control is also mainly based on a fixed compression / rebound stage opening ratio, which makes dynamic adjustment difficult. In addition, existing systems generally lack vibration energy recovery mechanisms and are unable to adaptively adjust power consumption under low-load conditions. They are also relatively weak in learning driver operating habits and in fault-tolerant handling of sensor failures.

[0003] However, the above-mentioned technical status quo has significant shortcomings: First, a single control strategy cannot take into account the damping force requirements under complex working conditions such as low-speed bumps, cornering roll, and emergency braking, resulting in difficulty in balancing vehicle ride smoothness and handling stability; second, the accuracy of multi-source data fusion is insufficient, and the error in working condition pattern recognition is large, causing the damping parameter matching to lag behind the actual road excitation; third, vibration energy is lost in large quantities in the form of heat energy, and there is a lack of a dynamic power consumption adjustment mechanism, resulting in waste of on-board energy; fourth, the system cannot adaptively learn the driver's operating habits, and sensor failure is likely to lead to control interruption, affecting driving safety and system reliability. Summary of the Invention

[0004] The object of the present invention is to overcome one or more deficiencies of the prior art and to provide a multi-modal directional adaptive control method for a shock absorber solenoid valve.

[0005] The object of the present invention is achieved through the following technical solutions:

[0006] A multi-modal directional adaptive control method for a shock absorber solenoid valve comprises the following steps:

[0007] S1. Perform multimodal data acquisition and preprocessing to collect vehicle acceleration, suspension displacement, steering angle, wheel speed, and road surface prediction signals from the forward-looking sensor;

[0008] S2. Based on the collected data, the operating mode is divided according to vehicle speed, main vibration frequency, and roll angle;

[0009] S3. Setting the damping direction opening ratios for the compression phase and the rebound phase according to the divided operating modes;

[0010] S4. Dynamically adjust parameters based on vibration amplitude and frequency to generate a control signal to drive the solenoid valve to the set opening direction;

[0011] S5. When the vibration energy is less than the threshold, the system switches to low-power mode and stores the electrical energy generated by the reverse motion of the solenoid valve in an onboard capacitor for use in high-load conditions.

[0012] S6. Record the driver's operating habits, optimize control parameters through algorithms, and switch to data-based virtual sensor estimation mode when a sensor fails.

[0013] Furthermore, the operating mode is divided according to the vehicle speed, main vibration frequency and roll angle in S2, specifically: the vehicle speed v, main vibration frequency f and roll angle θ are comprehensively judged by the preset operating mode division rules to determine whether the current operating mode belongs to the low-speed bump mode, the cornering roll mode or the emergency braking mode.

[0014] Furthermore, the working condition division rule satisfies the formula:

[0015]

[0016] Among them, Mode is the working mode, v is the vehicle speed, v th1 is the low speed threshold, f is the main vibration frequency, f th1 is the vibration frequency threshold, θ is the roll angle, θ th1 is the roll angle threshold, a dec is the deceleration, a th1 The deceleration threshold is based on a quantitative correlation between vehicle dynamics and operating conditions. For example, during low-speed bumps, the vehicle speed is low but the road excitation frequency is high (such as continuous potholes); roll in corners is directly reflected by the roll angle; and emergency braking is determined by sudden changes in deceleration. This transforms fuzzy operating condition characteristics into a quantitative judgment standard, avoiding the recognition errors caused by traditional single parameters (such as vehicle speed alone), improving the accuracy of operating condition classification, and providing an accurate basis for subsequent damping control.

[0017] Furthermore, the damping direction opening ratio between the compression stage and the rebound stage is set in S3. Specifically, the ratio of the compression stage opening to the rebound stage opening is set for different operating modes to achieve directional control of the damping force. Setting the compression stage opening ratio to the rebound stage opening for different operating conditions achieves directional control of the damping force, which solves the problem that traditional fixed openings cannot adapt to the damping requirements of different operating conditions.

[0018] Furthermore, the damping direction opening ratio satisfies the formula:

[0019]

[0020] Among them, k is the opening ratio in the damping direction, C compis the opening degree in the compression stage, C reb The ratio k is used to control the distribution of damping force in the compression / rebound phases, based on the fluid dynamics relationship between the damping force and the valve opening (the damping force is positively correlated with the square of the opening).

[0021] Furthermore, the dynamic parameter adjustment based on vibration amplitude and frequency in S4 specifically involves determining a parameter adjustment coefficient based on vibration amplitude A and frequency f, thereby dynamically optimizing the control parameters. This determination of the parameter adjustment coefficient based on vibration amplitude A and frequency f, and the optimization of the control parameters, addresses the problem of traditional fixed parameters being unable to respond to road surface excitation in real time.

[0022] Furthermore, the parameter adjustment coefficient satisfies the formula:

[0023]

[0024] Among them, α is the parameter adjustment coefficient, α0 is the initial adjustment coefficient, β and γ are weight coefficients, A is the vibration amplitude, A ref is the reference vibration amplitude, f is the vibration frequency, f ref The reference vibration frequency is derived from empirical modeling of the "vibration characteristics-control parameter" mapping relationship. Weight coefficients β and γ are fitted experimentally to directly correlate parameter adjustments with the road excitation intensity (amplitude) and frequency characteristics. As the vibration amplitude A increases, the coefficient α increases, accelerating damping force adjustment. As the vibration frequency f increases, the control parameters are dynamically compensated, shortening response delays (e.g., reducing damping force fluctuations during high-frequency jolts) and improving control accuracy.

[0025] Furthermore, the determination in S5 whether the vibration energy is less than a threshold value is specifically as follows: the vibration energy E is calculated and compared with the preset energy threshold E th For comparison, when E <E th By calculating the vibration energy E and comparing it with the threshold, it is determined whether to switch to low power mode, which solves the problems of energy waste and unadjustable power consumption in traditional technologies.

[0026] Furthermore, the vibration energy calculation satisfies the formula:

[0027]

[0028] Where E is the vibration energy, F(t) is the function of the damping force with respect to time, v(t) is the function of the vibration velocity with respect to time, and t is time. The basic principle of energy calculation (work = force × displacement, where the product of the integral damping force and the velocity is equivalent to the cumulative work of the damping force on the vibration displacement). To quantify the vibration energy, when E <E thWhen the vehicle is on a smooth road (such as on a smooth road), it switches to low power consumption mode to reduce energy consumption; combined with the reverse power generation of the solenoid valve (Faraday's law of electromagnetic induction), the vibration energy is converted into electrical energy storage, solving the problem of vibration energy being lost as heat in traditional technology.

[0029] Furthermore, S6 records the driver's operating habits and optimizes control parameters. Specifically, a machine learning algorithm is used to train the driver's operating data to generate a personalized control parameter optimization model, thereby achieving self-learning optimization of the control parameters. This training of the driver's operating data with a machine learning algorithm to generate a personalized control parameter model addresses the problem of traditional control strategies being unable to adapt to different driving styles, thereby improving system adaptability.

[0030] The beneficial effects of the present invention are:

[0031] (1) Through multimodal data fusion and working condition quantitative identification technology, accurate division of complex working conditions and adaptive adjustment of damping force direction are achieved, thereby improving the vehicle's driving smoothness and handling stability under different road conditions;

[0032] (2) Through dynamic parameter adjustment of vibration characteristics and energy recovery technology, the solenoid valve drive parameters are optimized in real time. Combined with low-power mode switching and electromagnetic reverse power generation, the system energy consumption is reduced and the vehicle energy utilization efficiency is improved;

[0033] (3) Through self-learning of driver operating habits and fault tolerance technology, personalized control parameters are generated using machine learning algorithms, combined with virtual sensor estimation mode, to enhance system adaptability and reliability and ensure uninterrupted control. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of a multi-modal directional adaptive control method for a shock absorber solenoid valve;

[0035] Figure 2 A flowchart of the specific steps of a multi-modal directional adaptive control method for a shock absorber solenoid valve provided in an embodiment;

[0036] Figure 3 A flowchart of the specific steps of a multi-modal directional adaptive control method for a shock absorber solenoid valve is provided in an embodiment. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0038] See Figure 1 , provides a multi-modal directional adaptive control method for a shock absorber solenoid valve, comprising the following steps:

[0039] S1. Perform multimodal data acquisition and preprocessing to collect vehicle acceleration, suspension displacement, steering angle, wheel speed, and road surface prediction signals from the forward-looking sensor;

[0040] S2. Based on the collected data, the operating mode is divided according to vehicle speed, main vibration frequency, and roll angle;

[0041] S3. Setting the damping direction opening ratios for the compression phase and the rebound phase according to the divided operating modes;

[0042] S4. Dynamically adjust parameters based on vibration amplitude and frequency to generate a control signal to drive the solenoid valve to the set opening direction;

[0043] S5. When the vibration energy is less than the threshold, the system switches to low-power mode and stores the electrical energy generated by the reverse motion of the solenoid valve in an onboard capacitor for use in high-load conditions.

[0044] S6. Record the driver's operating habits, optimize control parameters through algorithms, and switch to data-based virtual sensor estimation mode when a sensor fails.

[0045] In S2, the operating mode is divided according to the vehicle speed, main vibration frequency, and roll angle. Specifically, the vehicle speed v, main vibration frequency f, and roll angle θ are comprehensively judged through the preset operating mode division rules to determine whether the current operating mode belongs to the low-speed bump mode, the cornering roll mode, or the emergency braking mode.

[0046] The working condition division rule satisfies the formula:

[0047]

[0048] Among them, Mode is the working mode, v is the vehicle speed, v th1 is the low speed threshold, f is the main vibration frequency, f th1 is the vibration frequency threshold, θ is the roll angle, θ th1 is the roll angle threshold, a dec is the deceleration, a th1 is the deceleration threshold.

[0049] In S3, the damping direction opening ratio of the compression stage and the rebound stage is set. Specifically, for different working modes, the ratio of the compression stage opening to the rebound stage opening is set respectively to achieve directional control of the damping force.

[0050] The opening ratio in the damping direction satisfies the formula:

[0051]

[0052] Among them, k is the opening ratio in the damping direction, C comp is the opening degree in the compression stage, C reb It is the opening degree in the rebound stage.

[0053] In S4, the parameters are dynamically adjusted based on the vibration amplitude and frequency. Specifically, the parameter adjustment coefficient is determined by the vibration amplitude A and the frequency f, and the control parameters are dynamically optimized.

[0054] The parameter adjustment coefficient satisfies the formula:

[0055]

[0056] Among them, α is the parameter adjustment coefficient, α0 is the initial adjustment coefficient, β and γ are weight coefficients, A is the vibration amplitude, A ref is the reference vibration amplitude, f is the vibration frequency, f ref is the reference vibration frequency.

[0057] In step S5, it is determined whether the vibration energy is less than the threshold value. Specifically, the vibration energy E is calculated and compared with the preset energy threshold E. th For comparison, when E <E th Switches to low power mode when

[0058] The vibration energy calculation satisfies the formula:

[0059]

[0060] Where E is the vibration energy, F(t) is the damping force as a function of time, v(t) is the vibration velocity as a function of time, and t is time.

[0061] S6 records the driver's operating habits and optimizes control parameters. Specifically, it uses machine learning algorithms to train the driver's operating data and generate a personalized control parameter optimization model to achieve self-learning optimization of control parameters.

[0062] See Figure 2-Figure 3 , for the specific implementation steps:

[0063] S1 multimodal data acquisition and preprocessing:

[0064] S1.1 Data Acquisition System Architecture: In modern vehicle suspension control systems, a multimodal data acquisition system forms the foundation of adaptive control. This system utilizes a distributed sensor network architecture, working collaboratively to achieve comprehensive awareness of the vehicle's driving state. The body acceleration sensor, a core component, utilizes high-precision triaxial MEMS accelerometer technology. Its placement is carefully designed, encompassing not only the center of mass of the vehicle but also sensors located at the tops of the four suspension towers, forming a three-dimensional monitoring network. This arrangement simultaneously captures both the overall vehicle motion and localized vibration characteristics, providing rich information for subsequent operating condition identification.

[0065] The suspension displacement sensor utilizes a non-contact inductive design, offering high precision, high reliability, and a long lifespan. Installed between the suspension piston rod and the cylinder, it monitors real-time displacement changes during compression and rebound. The sensor utilizes a specialized packaging process to effectively resist the effects of harsh environmental factors such as dust and moisture, ensuring stable operation in a variety of complex road conditions.

[0066] The steering angle sensor, integrated into the steering column, utilizes advanced Hall-effect technology. It not only accurately measures the absolute steering wheel angle but also offers rapid response, enabling real-time detection of the driver's steering intent. Its sampling frequency takes vehicle dynamics into account, ensuring accurate steering information even in high-speed driving and aggressive maneuvers.

[0067] The wheel speed sensor utilizes an electromagnetic induction design, working in conjunction with the wheel ring gear to obtain real-time information on the rotational speed of each wheel. The sensor utilizes digital signal processing technology to effectively suppress electromagnetic interference and improve signal quality. Wheel speed information is not only used to calculate vehicle speed but also plays a vital role in vehicle dynamics control, such as the Anti-lock Braking System (ABS) and the Electronic Stability Program (ESP).

[0068] The forward-looking sensor, the system's "eyes," utilizes machine vision-based road surface prediction technology. Using a high-resolution camera, the sensor captures real-time images of the road surface at a certain distance ahead of the vehicle. Combined with advanced image processing algorithms, it identifies road features such as potholes, bumps, and cracks. Its carefully designed detection range and viewing angle ensure sufficient look-ahead time while ensuring accurate recognition, providing a reliable basis for pre-adjustment of the suspension system.

[0069] S1.2 Synchronous acquisition of multi-source signals: Synchronous acquisition of multi-source signals is a key step in achieving accurate working condition identification. In this system, the signals of each sensor are transmitted via the on-board CAN bus, and time-division multiplexing technology is used to achieve data synchronization. Different types of sensors are set with different sampling frequencies based on their signal characteristics and importance. Since the body acceleration sensor needs to capture high-frequency vibration signals, the sampling frequency is set to a relatively high value to ensure that the vehicle vibration characteristics can be fully recorded. The suspension displacement sensor has a moderate sampling frequency that can meet the needs of suspension dynamic response monitoring. The steering angle and wheel speed sensor sampling frequencies are set according to the vehicle's dynamic characteristics to ensure that they can accurately reflect the driver's operation and the vehicle's motion status. The image frame rate of the forward-looking sensor is optimized based on the processing power of the road feature recognition algorithm and the system response requirements.

[0070] To ensure temporal consistency among signals with different sampling frequencies, the system employs a hardware clock synchronization mechanism. A unified timestamp is set in the CAN bus controller, and the timestamp is embedded in the data from each sensor. After the data is transmitted to the central control unit (ECU), timestamp calibration is first performed. A specific algorithm is used to time-align the low-sampling-rate signals, eliminating errors caused by asynchronous sampling. This synchronization mechanism ensures temporal consistency among different sensor data, providing a reliable foundation for subsequent data fusion and operating condition identification.

[0071] S1.3 Signal Preprocessing: Signal preprocessing is an important step to improve data quality and enhance system reliability. In this system, the signal preprocessing process includes filtering and denoising, data normalization, and feature extraction.

[0072] Filtering and denoising utilizes different filtering algorithms tailored to the characteristics of different sensor signals. For vehicle acceleration signals, which contain significant high-frequency noise and resonance caused by the sensor's natural frequency, a second-order Butterworth low-pass filter is used. This filter exhibits excellent amplitude-frequency and phase-frequency characteristics, effectively suppressing high-frequency noise while maximizing the preservation of useful signal components. The cutoff frequency is optimized based on vehicle vibration characteristics and sensor performance, ensuring effective denoising without affecting the signal's key characteristics.

[0073] The suspension displacement signal is optimized using the Kalman filter algorithm. Kalman filtering is an optimal recursive data processing algorithm that uses the system's state equation and observation equation to optimally estimate the system state. In suspension displacement signal processing, the Kalman filter uses the suspension dynamics model to establish these equations, estimating the suspension's true displacement in real time. This effectively suppresses measurement noise and jitter, improving the accuracy and reliability of the displacement signal.

[0074] The steering angle signal undergoes phase compensation after filtering. Due to mechanical and signal transmission delays in the steering system, there is a phase difference between the steering angle signal and the vehicle's actual response. This signal delay is corrected by calculating the steering angle change rate, ensuring the timing of steering operation and operating condition recognition, and improving the system's response speed and accuracy to steering inputs.

[0075] Data normalization is a crucial step in standardizing the scale of sensor signals. By mapping the signal range to a specific interval, the impact of sensor range and unit differences on subsequent data processing and analysis is eliminated. The same normalization method is applied to body acceleration signals, suspension displacement signals, steering angle signals, and other signals, ensuring comparability of all parameters during data fusion and improving the accuracy and reliability of operating condition identification.

[0076] Road surface feature extraction is a key step in forward-looking sensor signal processing. Images captured by the forward-looking sensor are first grayscaled, converting color images to grayscale. This reduces data volume while preserving key image features. Histogram equalization is then performed to enhance image contrast and clarify road surface features. The Canny edge detection algorithm identifies road surface boundaries and feature edges, while the Hough transform detects straight and curved features in the road surface, extracting the geometric dimensions and location of road defects such as potholes and bumps. The extracted road surface feature parameters are converted into vibration excitation prediction signals in the time domain. These signals are then integrated with the real-time collected suspension displacement and acceleration signals to form forward-looking control inputs, providing a basis for pre-adjustment of the suspension system.

[0077] S2 working mode division:

[0078] S2.1 Multi-Dimensional Parameter Extraction: Operating mode classification is the core step in achieving adaptive shock absorber control, and multi-dimensional parameter extraction is the foundation for this classification. In this system, multiple key parameters are extracted from preprocessed data, including vehicle speed, main vibration frequency, and roll angle. These parameters reflect the vehicle's driving state and road conditions from different perspectives.

[0079] Vehicle speed is a key indicator of vehicle driving status and is calculated by taking the weighted average of the four-wheel speed signals. A special weighting algorithm is used to ensure accurate speed calculation, taking into account the difference in inside and outside wheel speeds during steering. Speed ​​information is not only used for operating condition identification but also plays a crucial role in adjusting damping control parameters. Different vehicle speeds place varying demands on the suspension system, and using speed parameters allows for dynamic adjustment of damping characteristics.

[0080] The dominant vibration frequency is a key parameter that reflects the road excitation characteristics and vehicle vibration status. By performing a Fast Fourier Transform (FFT) on the vehicle body's vertical acceleration signal, the time-domain signal is converted to the frequency domain. Its power spectral density (PSD) is calculated, and the frequency component with the largest energy contribution is taken as the dominant vibration frequency. The dominant vibration frequency can intuitively reflect the unevenness of the road surface. Different road surface types (such as smooth, bumpy, and speed bumps) generate vibration excitations of varying frequencies. By analyzing the dominant vibration frequency, the vehicle's current road surface condition can be accurately identified.

[0081] Roll angle is a key indicator of vehicle lateral stability. It is calculated using the inverse tangent function as the ratio of the vehicle's lateral acceleration signal to the acceleration due to gravity. Roll angle information reflects the degree of vehicle roll during cornering and is a key parameter for identifying cornering roll conditions. Excessive roll angles can cause vehicle instability during high-speed cornering. By monitoring the roll angle in real time and adjusting the shock absorber damping characteristics, body roll can be effectively suppressed, improving the vehicle's lateral stability and handling safety.

[0082] S2.2 Implementation of Operating Condition Classification Rules: Based on the extracted multi-dimensional parameters, the system uses clear operating condition classification rules to accurately identify different driving conditions. Based on specific operating condition classification formulas, vehicle driving conditions are divided into various types, such as low-speed bump mode, cornering roll mode, and emergency braking mode.

[0083] Low-speed bump mode identification is primarily based on vehicle speed and dominant vibration frequency. When the vehicle speed falls below a set low-speed threshold and the dominant vibration frequency exceeds a set frequency threshold, the system determines that the vehicle is in a low-speed bump condition. This condition typically occurs when the vehicle is traveling at low speed over a series of potholes, speed bumps, or other surfaces. During this time, the vehicle vibrates violently, significantly impacting ride comfort. By identifying this condition, targeted adjustments to the shock absorber's damping characteristics can be made to improve ride comfort.

[0084] Cornering roll mode identification is primarily based on roll angle parameters. When the roll angle exceeds a set threshold, the system determines that the vehicle is in a cornering roll condition. The roll angle threshold is determined through static vehicle roll testing and reflects the safe limit of vehicle roll during normal driving. When the vehicle's roll angle exceeds this threshold, it indicates that the degree of body roll is affecting driving stability, necessitating prompt adjustment of the shock absorber damping characteristics, increasing the rebound damping force of the outboard suspension to suppress body roll and improve cornering stability.

[0085] Emergency braking mode recognition is based on deceleration parameters. The deceleration is calculated by differentially calculating the vehicle speed signal. When the deceleration exceeds a set deceleration threshold, the system determines that the vehicle is in an emergency braking condition. The deceleration threshold is set based on the difference in deceleration between normal braking and emergency braking, effectively distinguishing between normal and emergency braking conditions. During emergency braking, the vehicle's center of gravity shifts forward, causing the front of the vehicle to significantly drop. By adjusting the damping characteristics of the front suspension and increasing the compression damping force, excessive frontal drop can be effectively suppressed, maintaining vehicle stability during braking and improving braking safety.

[0086] S2.3 Fuzzy Decision-Making for Operating Modes: To address the potential overlap of operating modes when using a single parameter, this system introduces a fuzzy logic decision-making mechanism. Fuzzy logic is a mathematical method for dealing with uncertainty and ambiguity that can simulate the ambiguity and uncertainty of human thinking, offering unique advantages in identifying complex operating conditions.

[0087] In the fuzzy decision-making process for operating modes, fuzzy subsets are first established for parameters such as vehicle speed, dominant vibration frequency, roll angle, and deceleration. For example, vehicle speed is divided into fuzzy subsets such as "low," "medium," and "high," dominant vibration frequency into fuzzy subsets such as "low frequency," "medium frequency," and "high frequency," roll angle into fuzzy subsets such as "small," "medium," and "large," and deceleration into fuzzy subsets such as "small," "medium," and "large." Each fuzzy subset has a corresponding membership function, which is used to calculate the degree to which the parameter belongs to that fuzzy subset.

[0088] By constructing a fuzzy rule table, fuzzy subsets of different parameters are combined to form a series of fuzzy rules. For example, if the vehicle speed belongs to the "low speed" fuzzy subset with a membership greater than a certain value, and the vibration dominant frequency belongs to the "high frequency" fuzzy subset with a membership greater than a certain value, the vehicle is judged to be in a low-speed bump mode. The fuzzy rules are formulated based on extensive test data and expert experience to ensure their rationality and effectiveness.

[0089] During the actual operating condition identification process, the membership degree of each parameter to each fuzzy subset is calculated based on its actual value. Then, reasoning is performed based on the fuzzy rule table to determine the membership degree of each operating condition mode. Finally, through defuzzification, the fuzzy reasoning results are converted into specific operating condition mode determination results. This fuzzy decision-making mechanism can effectively handle parameter fluctuations caused by sensor noise, reduce the rate of misjudgment of operating conditions, and improve the accuracy and reliability of operating condition identification.

[0090] S3 damping direction opening ratio setting:

[0091] S3.1 Establishing the Working Condition-Damping Mapping: Setting the damping opening ratio is a key technology for achieving adaptive shock absorber control. For different working conditions, this system establishes a ratio between the compression phase opening and the rebound phase opening, known as the damping opening ratio. This ratio is determined through a combination of extensive bench testing and on-road testing to ensure optimal damping force distribution under all conditions, balancing vehicle comfort and handling stability.

[0092] The damping opening ratio is determined based on a deep understanding of vehicle dynamics and shock absorber operating principles. During driving, the compression phase of the shock absorber primarily absorbs road impact energy, reducing vehicle vibration; while the rebound phase primarily controls the suspension's rebound velocity, preventing excessive vehicle rebound. By adjusting the ratio of compression to rebound opening, the damping characteristics of the shock absorber can be specifically optimized at different stages to meet vehicle performance requirements under varying operating conditions.

[0093] S3.2 Low-Speed ​​Bump Mode Opening Ratio Setting: In Low-Speed ​​Bump Mode, the vehicle is traveling at a low speed but on poor road conditions with obstacles such as potholes and speed bumps, resulting in more severe vehicle vibration. To improve ride comfort, the suspension absorbs road impact faster and reduces the duration of vehicle vibration.

[0094] Under these operating conditions, the system increases the opening in the compression phase while appropriately reducing the opening in the rebound phase, maintaining a damping-direction opening ratio greater than 1. In specific implementation, the corresponding opening ratio is selected using a table lookup based on the dominant vibration frequency and amplitude. When the dominant vibration frequency and amplitude are high, indicating severe road impact, the damping-direction opening ratio is increased to further reduce the damping force in the compression phase and accelerate the suspension's absorption of impact energy. Meanwhile, the damping force in the rebound phase is appropriately increased to control the suspension's rebound speed, reducing the amplitude and duration of body vibration and improving ride comfort.

[0095] S3.3 Curve Roll Mode Aperture Ratio Setting: When cornering, the vehicle is subject to centrifugal force, causing the body to tilt outward, affecting vehicle handling stability and ride comfort. To suppress body roll, the rebound damping force of the outboard suspension is increased, limiting the suspension's rebound travel and thus reducing the body roll angle.

[0096] In cornering roll mode, the system reduces the rebound opening of the outboard shock absorber, increasing the damping opening ratio. Specifically, the opening ratio is dynamically adjusted based on the roll angle. When the roll angle is small, the opening ratio adjustment is small; as the roll angle increases, the adjustment gradually increases. This dynamic adjustment increases the rebound damping force of the outboard suspension as the roll angle increases, effectively suppressing body roll and improving the vehicle's lateral stability and handling safety.

[0097] At the same time, in order to ensure the smoothness of the vehicle after driving on a curve, when the vehicle exits the curve, the system will gradually adjust the damping direction opening ratio according to the changing trend of the roll angle, so that the shock absorber damping characteristics can smoothly transition to the normal driving state, avoiding body vibration and ride discomfort caused by sudden changes in damping force.

[0098] S3.4 Emergency Braking Mode Opening Ratio Setting: During emergency braking, the vehicle's center of gravity shifts forward, causing the front end to significantly dip. This can result in excessive front wheel load, impacting braking performance and vehicle stability. To prevent excessive front end dip, the front suspension's compression damping force is increased, limiting the front suspension's compression stroke to maintain vehicle stability during braking.

[0099] In emergency braking mode, the system reduces the front suspension's compression opening, reducing the damping ratio. Proportional-integral control (PI) adjusts the opening ratio based on deceleration. When deceleration is low, the opening ratio adjustment is small; as deceleration increases, the adjustment gradually increases. This dynamic adjustment increases the front suspension's compression damping force as deceleration increases, effectively suppressing vehicle nose dive, maintaining vehicle stability during braking, and enhancing braking safety.

[0100] At the same time, in order to ensure the smoothness of the vehicle after braking, when braking is completed, the system will gradually adjust the damping direction opening ratio according to the changing trend of vehicle speed and acceleration, so that the shock absorber damping characteristics can smoothly transition to the normal driving state, avoiding body vibration and ride discomfort caused by sudden changes in damping force.

[0101] S4 dynamic parameter adjustment and control signal generation:

[0102] S4.1 Vibration Characteristic Parameter Extraction: Dynamic parameter adjustment is a crucial step in achieving adaptive shock absorber control, and vibration characteristic parameter extraction forms the foundation for this dynamic parameter adjustment. In this system, key parameters such as vibration amplitude and frequency are extracted from vehicle acceleration and suspension displacement signals. These parameters intuitively reflect the vehicle's vibration state and road excitation characteristics.

[0103] The vibration amplitude is obtained by calculating the root mean square (RMS) value of the acceleration signal. The RMS value is a commonly used statistic that reflects the average energy level of a signal. In vibration signal processing, larger RMS values ​​indicate greater vibration amplitude and stronger road excitation to the vehicle. By calculating the vibration amplitude in real time, the damping characteristics of the shock absorber can be dynamically adjusted to ensure that the vehicle maintains excellent ride comfort and handling stability under various road conditions.

[0104] The vibration frequency is determined by performing spectral analysis on the acceleration signal. Fast Fourier Transform (FFT) is used to convert the time-domain signal into a frequency-domain signal, and its power spectral density (PSD) is calculated to analyze the signal's frequency content and energy distribution. The frequency band where the energy is concentrated is considered the dominant frequency, as this frequency reflects the primary characteristics of the road excitation. Different road types produce vibration excitations of varying frequencies. For example, smooth roads produce lower vibration frequencies, while bumpy roads produce higher vibration frequencies. By analyzing the vibration frequency, the road type can be identified, allowing for targeted adjustments to the shock absorber's damping characteristics and improving the vehicle's adaptability to different road surfaces.

[0105] S4.2 Parameter Adjustment Coefficient Calculation: Based on the extracted vibration amplitude and frequency parameters, the system uses a specific formula to calculate the parameter adjustment coefficient. This coefficient is used to dynamically adjust the shock absorber's control parameters, enabling the shock absorber's damping characteristics to be optimized in real time based on the vehicle's vibration state and road excitation characteristics.

[0106] The calculation formula for the parameter adjustment coefficient comprehensively considers the impact of vibration amplitude and frequency on vehicle performance. Each parameter in the formula is determined through extensive test data and optimization algorithms to ensure that the coefficient calculation accurately reflects the actual vehicle requirements. As the vibration amplitude increases, the parameter adjustment coefficient increases accordingly, increasing the shock absorber's damping force and improving vehicle stability. As the vibration frequency increases, the parameter adjustment coefficient also increases, enabling the shock absorber to respond more quickly to road excitation and reducing body vibration.

[0107] The calculation of the parameter adjustment coefficients also considers the influence of the reference vibration amplitude and reference vibration frequency. The reference values ​​are based on the vehicle's vibration characteristics under standard road excitation conditions. By comparing the actual vibration parameters with the reference values, the vehicle's vibration state can be more accurately assessed and the shock absorber's damping characteristics can be appropriately adjusted.

[0108] S4.3 Dynamic Optimization of Control Parameters: Based on the calculated parameter adjustment coefficients, this system dynamically optimizes control parameters such as the duty cycle and drive voltage of the solenoid valve drive signal. These control parameters directly affect the opening degree and response speed of the solenoid valve, thereby controlling the damping force of the shock absorber.

[0109] Taking the duty cycle as an example, its adjustment formula is designed based on the parameter adjustment coefficient. As the parameter adjustment coefficient increases, the duty cycle increases accordingly, extending the solenoid valve's opening time and increasing its opening, thereby increasing the damping force. Conversely, as the parameter adjustment coefficient decreases, the duty cycle decreases accordingly, shortening the solenoid valve's opening time and decreasing its opening, thereby reducing the damping force. This dynamic adjustment method allows the shock absorber's damping force to be optimized in real time based on the vehicle's vibration state and road excitation characteristics, improving vehicle ride comfort and handling stability.

[0110] Furthermore, to account for the solenoid valve's response delay, this system incorporates an advance compensation mechanism. This compensation time is calculated based on the vibration frequency, and the drive signal is adjusted in advance to compensate for the solenoid valve's action delay, synchronizing the damping force change with road excitation. This advance compensation technology effectively improves system response speed, reduces control errors caused by the solenoid valve's delayed response, and enhances shock absorber control accuracy.

[0111] S4.4 Control Signal Generation and Drive: The dynamically optimized control parameters are ultimately converted into a PWM (Pulse Width Modulation) signal, which is used to drive the solenoid valve. PWM is a commonly used digital control signal that adjusts the pulse width to control the average voltage and current of the solenoid valve, thereby achieving precise control of the valve's opening.

[0112] The PWM signal frequency is carefully chosen to ensure the solenoid valve responds to signal changes and achieves smooth damping force adjustment, while also avoiding excessive frequency that could cause solenoid valve heating and increased energy consumption. In this system, the PWM signal frequency is set to an appropriate value, ensuring stable solenoid valve operation under various operating conditions and precise control of the shock absorber's damping force.

[0113] The drive circuit utilizes an H-bridge topology, using MOSFET power transistors to achieve bidirectional drive of the solenoid valve. The H-bridge circuit boasts simple structure, high efficiency, and strong reliability. It controls both forward and reverse currents in the solenoid valve, supporting its reverse power generation function. A soft-start mechanism is incorporated into the signal generation process to prevent current surges caused by sudden changes in the drive signal, thereby extending the life of the solenoid valve. The drive circuit also incorporates overcurrent, overvoltage, and temperature protection features, ensuring safe and reliable system operation under various abnormal conditions.

[0114] S5 low power mode switching and energy recovery:

[0115] S5.1 Vibration Energy Calculation: During vehicle operation, the suspension system is constantly stimulated by the road surface, generating vibrations. This vibration energy is typically dissipated as heat. This system innovatively incorporates vibration energy recovery technology to recycle this previously wasted energy, improving the vehicle's energy efficiency.

[0116] Vibration energy calculations are based on a specific formula that comprehensively considers the temporal relationship between damping force and vibration velocity. Real-time vibration energy values ​​are calculated by combining the velocity signal from the suspension displacement sensor with the damping force model. The damping force model utilizes the Bingham plasticity model, which accurately describes the damping characteristics of the shock absorber under different operating conditions and provides a reliable theoretical basis for vibration energy calculations.

[0117] Vibration energy is calculated using numerical integration. This method integrates the product of the damping force and the vibration velocity within a short time window to obtain the vibration energy within that time period. The selection of the integration time window requires a comprehensive consideration of both computational accuracy and real-time performance requirements to ensure accurate vibration energy calculation while meeting the system's real-time control requirements.

[0118] S5.2 Low-Power Mode Switching Logic: To reduce system energy consumption and extend vehicle battery life, this system introduces low-power mode switching logic. By comparing the calculated vibration energy with a preset energy threshold, the system determines the vehicle's current driving condition and decides whether to switch to low-power mode.

[0119] When the vibration energy falls below the threshold, indicating the vehicle is traveling on a relatively smooth road with minimal vibration, the system switches to low-power mode. In low-power mode, the central control unit (ECU) enters a dormant state, retaining only the wake-up function for key sensors, reducing overall system power consumption. Simultaneously, the supply voltage to the solenoid valve drive circuit is lowered, reducing power consumption. Image acquisition from the forward-facing sensor is disabled, retaining only essential signals such as wheel speed and steering angle, further reducing system energy consumption.

[0120] The transition from low-power mode to low-power mode uses a smooth transition strategy to avoid system instability caused by the mode switch. During the transition, the system gradually adjusts the operating status of each component to ensure that vehicle performance is not affected. If the vibration energy exceeds the energy threshold again, the system automatically switches from low-power mode to normal operation and resumes all functions.

[0121] S5.3 Solenoid Valve Reverse Power Generation Control: When the system detects vibration energy exceeding the energy threshold, indicating that the vehicle is traveling on a bumpy road and experiencing significant vibration, the solenoid valve reverse power generation mode is activated. This utilizes the electromagnetic induction phenomenon generated by the solenoid valve coil moving in a magnetic field to convert vibration energy into electrical energy.

[0122] In reverse power generation mode, the solenoid valve's drive signal is controlled to operate in a power generation state. According to the law of electromagnetic induction, when a coil moves in a magnetic field, an induced electromotive force is generated across the coil. By adjusting the rate of change of the coil current, the generated power can be controlled. The generated electrical energy is converted to a DC voltage through a rectifier and filter circuit and stored in the onboard supercapacitor.

[0123] Supercapacitors offer advantages such as fast charge and discharge, long life, and high energy density, making them ideal for storing vibration-recovered energy. The capacitor capacity is designed based on the vehicle's average vibration energy requirements, ensuring it can store recovered energy for at least a certain period of time under high-energy conditions. During the energy recovery process, the system monitors the supercapacitor's charge level in real time to prevent overcharging.

[0124] S5.4 Energy Management Strategy: To achieve efficient energy utilization, this system has established a comprehensive energy management strategy. This strategy is based on three modes: energy recovery mode, low power mode, and normal operation mode. It dynamically switches the system operating mode according to the vibration energy level and the supercapacitor charge state.

[0125] When the supercapacitor charge falls below a certain threshold, the system prioritizes energy recovery mode, improving power generation efficiency and replenishing the supercapacitor as quickly as possible. In energy recovery mode, the system optimizes the solenoid valve control parameters to maximize the conversion of vibration energy into electrical energy.

[0126] When the supercapacitor charge exceeds a certain threshold, the system reduces power generation to prevent overcharging. At this point, the system allocates energy appropriately based on the vehicle's driving conditions and energy requirements, ensuring efficient energy utilization.

[0127] Under high-load conditions, such as emergency braking or continuous jolting, the system automatically draws power from the supercapacitor to replenish the solenoid valve drive energy, reducing the load on the vehicle battery. This energy allocation strategy effectively improves the vehicle's energy efficiency, extends battery life, and enhances system reliability and stability.

[0128] S6 driver habit learning and fault tolerance:

[0129] S6.1 Driver Operation Data Collection and Storage: Driver habit learning is a key technology for achieving personalized vehicle control, and driver operation data collection and storage is the foundation for driver habit learning. This system establishes a comprehensive driver operation database, collecting various driver operation signals in real time and efficiently storing and managing them.

[0130] The collected operating signals include steering angle, accelerator pedal opening, brake pedal pressure, and vehicle speed, which can reflect the driver's driving style and operating habits. The system also records system response data, such as corresponding operating modes and damping control parameters, to analyze the relationship between driver operation and system response.

[0131] The data collection cycle is carefully designed to ensure that subtle changes in the driver's operation can be captured while avoiding excessive data volume that would make storage and processing difficult. In this system, the data collection cycle is set to an appropriate value to meet the needs of driver habit learning.

[0132] After each trip, the system stores the collected data in batches. To improve storage efficiency, a circular storage mechanism is used to retain data from the most recent number of trips. The collected signals are also compressed using advanced compression algorithms. This significantly reduces the data size while preserving the signal's key characteristics, thus reducing storage space usage.

[0133] S6.2 Machine Learning Model Training: Based on collected driver operation data, this system uses a supervised learning algorithm to train a machine learning model to identify and predict driver habits. Supervised learning is a machine learning method based on labeled data. It establishes a mapping relationship between input and output by learning from known input and output data.

[0134] During model training, the collected data is first preprocessed, including steps such as data cleaning, feature extraction, and label generation. Data cleaning is used to remove noise and outliers, improving data quality. Feature extraction extracts representative features from the raw data, reducing data dimensionality and improving model training efficiency. Label generation assigns a corresponding label to each sample based on the driver's operating habits and system response data.

[0135] The model architecture uses a multilayer perceptron (MLP), a commonly used artificial neural network model with powerful nonlinear mapping capabilities. The MLP consists of an input layer, hidden layers, and an output layer, where multiple hidden layers are possible. Model performance can be optimized by adjusting the number of neurons and layers in the hidden layers.

[0136] During training, an adaptive learning rate optimization algorithm, such as the Adam algorithm, is used, with the mean squared error (MSE) as the loss function. This algorithm automatically adjusts the learning rate based on the error during training, improving training efficiency and model stability. When the loss function converges below a preset threshold, training is terminated, resulting in a trained machine learning model.

[0137] S6.3 Control Parameter Self-Learning Optimization: Based on a trained machine learning model, this system implements self-learning optimization of control parameters. When the driver performs steering, braking, and other maneuvers, the model predicts the optimal damping control parameters based on the current operating characteristics and compares them with the system default parameters. If the difference exceeds a certain threshold, the parameter optimization process is initiated.

[0138] The optimized parameters are applied to the system through a gradual update process, with each update amplitude controlled within a certain range to avoid fluctuations in control performance caused by sudden changes in parameters. This gradual update strategy enables the system to smoothly adapt to the driver's operating habits while ensuring vehicle safety and stability.

[0139] The system regularly retrains its machine learning model to adapt to changes in the driver's operating habits. As driving experience accumulates and driving style evolves, so too do drivers' operating habits. Regularly retraining the model allows these changes to be captured and control parameters adjusted to maintain system adaptability and optimize performance.

[0140] S6.4 Sensor Fault Detection and Isolation: To improve system reliability and safety, this system utilizes a combination of redundant design and fault detection algorithms to achieve real-time detection and isolation of sensor faults. Redundant design involves placing multiple identical or similar sensors in key locations. If one sensor fails, data from the remaining sensors can be used to continue operation.

[0141] The fault detection algorithm uses a Kalman filter residual detection method. The Kalman filter provides an optimal estimate of the system state. By comparing the residuals between the actual measured value and the estimated value, it can determine whether a sensor fault has occurred. When the covariance of the residuals exceeds a preset threshold, the sensor is considered faulty.

[0142] To improve the accuracy and reliability of fault detection, this system also combines time series analysis and statistical test methods. By analyzing the time series characteristics and statistical distribution characteristics of sensor data, it can more accurately identify sensor faults and distinguish fault types.

[0143] When a sensor failure is detected, the system immediately takes isolation measures, stops using the faulty sensor's data, and issues an alarm signal to notify the driver. Simultaneously, the system automatically switches to a backup sensor or uses other methods to estimate the faulty sensor's data to ensure the system can continue to operate normally.

[0144] S6.5 Virtual Sensor Estimation Mode Switch: When a sensor fails and no backup sensor is available, the system automatically switches to data-based virtual sensor estimation mode. A virtual sensor is a software sensor based on mathematical models and algorithms that estimates the output of a failed sensor using signals from other functioning sensors.

[0145] Virtual sensors use a state-space model, leveraging the inherent relationship between vehicle dynamics and sensor data to establish the system's state equations and observation equations. By solving these equations, the output value of a failed sensor can be estimated in real time.

[0146] In virtual sensor estimation mode, the system dynamically adjusts the parameters of the estimation model based on data from other functioning sensors to improve estimation accuracy. To ensure the reliability of the estimated values, the system verifies and evaluates the results in real time. If the estimation error exceeds a certain threshold, appropriate measures will be taken, such as reducing system performance requirements or issuing another alarm signal.

[0147] After switching to virtual sensor mode, the system maintains normal shock absorber control, ensuring vehicle stability is not affected by sensor failure. This fault-tolerant mechanism significantly improves system reliability and safety, reducing the risk of vehicle failure and accidents caused by sensor failure.

[0148] This embodiment fully demonstrates the implementation of a multi-modal directional adaptive control method for a shock absorber solenoid valve. This method uses a multi-modal data acquisition system to acquire real-time vehicle status information, employs a fuzzy logic decision-making mechanism to accurately identify complex operating conditions, optimizes shock absorber performance based on a dynamic adjustment strategy for the damping direction opening ratio, combines dynamic parameter adjustment with control signal generation techniques to ensure system response speed, employs low-power modes and energy recovery technology to improve energy efficiency, and enhances system adaptability and reliability through driver habit learning and fault tolerance mechanisms.

[0149] This method overcomes the limitations of traditional single-mode shock absorber control and enables adaptive adjustment under multiple operating conditions. By establishing a relationship between the opening ratios of the compression and rebound phases, it optimizes damping force distribution for different driving scenarios, effectively balancing vehicle comfort and handling stability. This precise parameter adjustment and control strategy significantly improve vehicle performance in critical driving conditions, such as low-speed bumps, cornering roll, and emergency braking.

[0150] In terms of engineering practicality, this approach fully considers various practical challenges. Multi-sensor fusion technology ensures data comprehensiveness and accuracy, while hardware redundancy and fault tolerance mechanisms enhance system reliability. Driver habit learning enables the system to automatically adapt to different driving styles, further enhancing the user experience. Furthermore, low-power mode and energy recovery technology align with energy conservation and environmental protection trends, offering significant economic and social benefits.

[0151] Real-vehicle testing has verified that this control method effectively improves vehicle performance under various typical operating conditions. Compared with traditional passive suspension, it significantly reduces the RMS vertical acceleration, the peak roll angle, and the amount of front-end dive during emergency braking. In terms of energy efficiency, the energy recovery system effectively reduces vehicle energy consumption, and the low-power mode significantly reduces system standby power consumption.

[0152] In summary, the multi-modal directional adaptive control method for a shock absorber solenoid valve provided in this embodiment demonstrates significant innovation and engineering practicality, effectively improving vehicle ride comfort, handling stability, and energy efficiency, and possessing broad market application prospects. This method is applicable not only to traditional fuel vehicles but also to new energy vehicles, providing new insights and technical solutions for the future development of vehicle suspension systems.

[0153] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A multi-modal directional adaptive control method for a shock absorber solenoid valve, characterized in that: The following steps are involved: S1. Perform multimodal data acquisition and preprocessing to collect vehicle acceleration, suspension displacement, steering angle, wheel speed, and road surface prediction signals from the forward-looking sensor; S2. Based on the collected data, the operating mode is divided according to vehicle speed, main vibration frequency, and roll angle; S3. Setting the damping direction opening ratios for the compression phase and the rebound phase according to the divided operating modes; S4. Dynamically adjust parameters based on vibration amplitude and frequency to generate a control signal to drive the solenoid valve to the set opening direction; S5. When the vibration energy is less than the threshold, the system switches to low-power mode and stores the electrical energy generated by the reverse motion of the solenoid valve in an onboard capacitor for use in high-load conditions. S6. Record the driver's operating habits, optimize control parameters through algorithms, and switch to data-based virtual sensor estimation mode when a sensor fails.

2. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 1, characterized in that: In said S2, the operating mode is divided according to the vehicle speed, the main vibration frequency, and the roll angle. Specifically, a comprehensive judgment is made on the vehicle speed v, the main vibration frequency f, and the roll angle θ according to the preset operating mode division rules to determine whether the current operating mode belongs to the low-speed bumping mode, the cornering roll mode, or the emergency braking mode.

3. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 2, characterized in that: The working condition division rule satisfies the formula: Among them, Mode is the working mode, v is the vehicle speed, v th1 is the low speed threshold, f is the main vibration frequency, f th1 is the vibration frequency threshold, θ is the roll angle, θ th1 is the roll angle threshold, a dec is the deceleration, a th1 is the deceleration threshold.

4. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 1, characterized in that: The damping direction opening ratio of the compression stage and the rebound stage is set in S3. Specifically, for different working modes, the ratio of the compression stage opening to the rebound stage opening is set respectively to achieve directional control of the damping force.

5. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 4, characterized in that: The damping direction opening ratio satisfies the formula: Among them, k is the opening ratio in the damping direction, C comp is the opening degree in the compression stage, C reb It is the opening degree in the rebound stage.

6. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 1, characterized in that: The dynamic adjustment of parameters based on the vibration amplitude and frequency in S4 is specifically as follows: the parameter adjustment coefficient is determined by the vibration amplitude A and the frequency f, and the control parameters are then dynamically optimized.

7. The multi-modal directional adaptive control method for a shock absorber solenoid valve according to claim 6, characterized in that: The parameter adjustment coefficient satisfies the formula: Among them, α is the parameter adjustment coefficient, α0 is the initial adjustment coefficient, β and γ are weight coefficients, A is the vibration amplitude, A ref is the reference vibration amplitude, f is the vibration frequency, f ref is the reference vibration frequency.

8. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 1, characterized in that: The determination in S5 whether the vibration energy is less than the threshold is specifically as follows: the vibration energy E is calculated and compared with the preset energy threshold E th For comparison, when E <E th Switches to low power mode when 9. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 8, characterized in that: The vibration energy calculation satisfies the formula: Where E is the vibration energy, F(t) is the damping force as a function of time, v(t) is the vibration velocity as a function of time, and t is time.

10. The multi-modal direction adaptive control method for a shock absorber solenoid valve according to claim 1, characterized in that: The S6 records the driver's operating habits and optimizes the control parameters, specifically: using a machine learning algorithm to train the driver's operating data to generate a personalized control parameter optimization model to achieve self-learning optimization of the control parameters.

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