Vehicle-mounted magnetorheological active suspension damping matching control system

Abstract

The invention relates to the technical field of vehicle engineering, and discloses a vehicle-mounted magneto-rheological active suspension damping matching control system which comprises a sensing module, a central control module, a magneto-rheological execution module and a power supply module. The signal output end of the central control module is electrically connected with the signal input end of the magneto-rheological execution module, and the power supply module is electrically connected with the sensing module, the central control module and the magneto-rheological execution module and provides working electric energy. All-dimensional parameters such as road surface states, vehicle body postures and vehicle operation are collected through the multi-source sensing module, signal interference and measurement errors are eliminated through weighted fusion processing of the central control module, and accurate decision of damping adjustment is achieved in combination with a pre-stored damping matching strategy library and a self-adaptive learning algorithm. And in cooperation with the stepless regulation characteristic of the magneto-rheological execution module, the driving state change can be followed in real time to dynamically optimize the damping force.

Description

Technical Field

[0001] This invention relates to the field of vehicle engineering technology, specifically to an on-board magnetorheological active suspension damping matching control system. Background Technology

[0002] With the rapid development of the automotive industry, consumers have increasingly higher requirements for vehicle ride comfort, handling stability, and driving safety. As the core component connecting the vehicle body and wheels, the performance of the suspension system directly determines the vehicle's ride quality. Therefore, active suspension systems with dynamic damping adjustment capabilities are gradually replacing traditional passive suspensions and have become a research hotspot. Among them, magnetorheological active suspensions, which use magnetorheological fluid as the damping adjustment medium, have advantages such as fast response speed, wide damping force adjustment range, and compact structure, and have been initially applied in mid-to-high-end vehicles.

[0003] In existing technologies, magnetorheological active suspension control systems mostly collect vehicle driving parameters through single or a few sensors, such as relying solely on vehicle speed sensors and body acceleration sensors to obtain basic data. After simple data processing, they output control signals based on a preset fixed damping adjustment strategy to drive the magnetorheological damper to complete damping adjustment. Although some improved solutions introduce multi-sensor data acquisition, they lack an effective data fusion and processing mechanism, making them susceptible to road interference signals and sensor measurement errors, resulting in insufficient data accuracy. Furthermore, the adjustment strategies they employ are mostly experience-based fixed parameter matching modes, which cannot adapt to different road conditions. The dynamic changes in driving conditions and vehicle operating status lead to adaptive damping matching, resulting in poor damping adjustment accuracy and response time, making it difficult to balance driving smoothness and handling stability in different scenarios. Furthermore, the architecture of existing control systems is mostly customized, with poor compatibility between functional modules, making it difficult to adapt to the installation and usage requirements of different vehicle models, resulting in low universality and increased vehicle adaptation costs. In addition, existing systems generally lack comprehensive fault monitoring and emergency handling mechanisms. When sensors, actuators, or control modules malfunction, damping adjustment can easily fail, leading to sudden changes in vehicle posture and affecting driving safety. In summary, improving the accuracy, scenario adaptability, and system reliability of magnetorheological active suspension damping matching has become a pressing technical problem to be solved in this field. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an on-board magnetorheological active suspension damping matching control system, which solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an on-board magnetorheological active suspension damping matching control system, comprising a sensing module, a central control module, a magnetorheological execution module, and a power supply module. The sensing module is bidirectionally electrically connected to the central control module, and the signal output terminal of the central control module is electrically connected to the signal input terminal of the magnetorheological execution module. The power supply module is electrically connected to the sensing module, the central control module, and the magnetorheological execution module respectively and provides operating power. The sensing module is used to collect road surface state parameters, vehicle posture parameters, and vehicle operating parameters during vehicle operation. The central control module has a built-in damping matching strategy library and an adaptive learning algorithm, which is used to fuse and analyze the parameters collected by the sensing module and generate a target damping adjustment signal. The magnetorheological execution module is used to respond to the target damping adjustment signal to achieve stepless adjustment of the damping force. The damping matching strategy library pre-stores damping reference parameters and dynamic correction rules for different driving scenarios.

[0006] Preferably, the perception module includes a pre-aiming perception unit, a vehicle body status perception unit, and an operating parameter perception unit. The pre-aiming perception unit includes a lidar and a high-definition camera, both of which are installed at the front of the vehicle's front compartment to collect road surface undulation data and obstacle distribution data within a range of 5-100 meters ahead. The vehicle body status perception unit includes a three-axis acceleration sensor installed at the vehicle's center of gravity, displacement sensors installed on the tops of each suspension tower, and an angular velocity sensor installed on the steering knuckle to collect the vehicle's vertical acceleration, pitch acceleration, roll acceleration, suspension compression, and wheel steering angular velocity. The operating parameter perception unit includes a vehicle speed sensor, wheel speed sensors, throttle opening sensor, and brake pressure sensor that communicate with the vehicle's CAN bus to collect real-time vehicle speed, wheel speed, power output status, and braking status.

[0007] Preferably, the central control module includes a data fusion processing unit, a damping decision unit, and a strategy optimization unit. The data fusion processing unit is used to perform spatiotemporal synchronous calibration and noise filtering on the multi-source parameters collected by the sensing module, and to eliminate road interference signals and sensor measurement errors through a weighted Kalman filter algorithm. The damping decision unit calls the damping matching strategy library, calculates the initial damping value corresponding to the current driving scenario based on the fused parameters, and then dynamically corrects it based on historical adjustment data and real-time vehicle response through the adaptive learning algorithm to generate the final target damping adjustment signal. The strategy optimization unit is used to update the correction rules in the damping matching strategy library based on the vehicle's cumulative driving data.

[0008] Preferably, the magnetorheological actuation module includes four independent magnetorheological dampers, which are respectively installed in the suspension system of the four wheels of the vehicle. Each magnetorheological damper includes a cylinder, a piston rod, a magnetorheological fluid chamber, an electromagnetic coil, and a displacement feedback sensor. The magnetorheological fluid chamber contains a customized magnetorheological fluid. The electromagnetic coil is wound in the coil groove in the middle of the cylinder. The input end of the electromagnetic coil is electrically connected to the signal output end of the central control module. The displacement feedback sensor is installed at the connection between the piston rod and the cylinder to collect the real-time extension and contraction of the damper and feed it back to the central control module. The customized magnetorheological fluid is made of nano-sized soft iron particles, silicone oil carrier, and anti-settling additives, and its viscosity can continuously change from 10 to 1000 mPa·s within the magnetic field strength range of 0-2T.

[0009] Preferably, the adaptive learning algorithm includes a scene recognition sub-algorithm and a parameter optimization sub-algorithm. The scene recognition sub-algorithm classifies the fused driving parameters using a support vector machine model to identify five typical driving scenarios: smooth roads, continuous bumps, rapid acceleration, rapid braking, and sharp turns. The parameter optimization sub-algorithm is based on the particle swarm optimization algorithm and uses the minimum root mean square value of the vehicle's vertical acceleration and the minimum fluctuation of tire ground pressure as dual objective functions to iteratively optimize the initial damping value. The optimization step size is dynamically adjusted according to the scene change rate. When the scene change rate is greater than 0.5 m / s, the optimization step size is shortened to 0.01 N·s / mm.

[0010] Preferably, the processing flow of the data fusion processing unit includes: First, aligning the timestamps of the parameters collected by each sensor to ensure that the data acquisition time difference does not exceed 1ms; Second, using an amplitude limiting filter to remove abnormal data that exceeds a reasonable range, the reasonable range being determined by the measurement range of each sensor and the vehicle dynamics characteristics; Third, fusing the data from different sensors of the same source using a weighted Kalman filter algorithm, with the weighting coefficients dynamically allocated according to the sensor's measurement accuracy, the weighting coefficient for the road surface undulation data from the lidar being 0.6, and the weighting coefficient for the road surface undulation data from the high-definition camera being 0.4; Fourth, outputting the fused unified parameter set to the damping decision unit.

[0011] Preferably, each of the magnetorheological dampers is equipped with an independent drive circuit, which includes a D / A conversion module, a power amplification module, and a constant current control module. The D / A conversion module is used to convert the digital damping adjustment signal output by the central control module into an analog voltage signal. The power amplification module is used to amplify the analog voltage signal to a drive voltage range of 0-12V. The constant current control module is used to convert the drive voltage into a constant current of 0-5A and output it to the electromagnetic coil to ensure that the stability error of the magnetic field strength generated by the electromagnetic coil does not exceed ±2%.

[0012] Preferably, the power supply module includes a vehicle-mounted 12V power supply, a DC / DC converter, and a backup power supply. The DC / DC converter converts the voltage of the vehicle-mounted 12V power supply into stable voltages of 5V, 12V, and 24V, respectively, to power the sensing module, the central control module, and the magnetorheological actuator module. The backup power supply is a supercapacitor bank. When the voltage of the vehicle-mounted 12V power supply drops below 10V, the backup power supply automatically starts working to ensure continuous system operation for at least 10 seconds. The capacity of the supercapacitor bank is at least 500F.

[0013] Preferably, the system also includes a fault diagnosis module, which is bidirectionally electrically connected to the central control module. The fault diagnosis module is used to monitor the working status of the sensing module, the central control module, and the magnetorheological actuator module in real time, and to collect the power supply voltage, operating current, and signal transmission status of each module. When an abnormal state is detected, a fault code is generated and fed back to the central control module. The central control module outputs a corresponding emergency damping adjustment signal according to the fault level, and at the same time sends the fault code to the vehicle instrument panel for display.

[0014] Preferably, the fault levels are divided into three levels. Level 1 faults are sensor signal drift, and the corresponding emergency strategy is to replace the current abnormal sensor data with the historical average value and maintain the damping value stable. Level 2 faults are failure of a single magnetorheological damper, and the corresponding emergency strategy is to adjust the damping value of the other magnetorheological damper on the same side to a safe reference value, while reducing the maximum permissible vehicle speed. Level 3 faults are partial failure of the central control module, and the corresponding emergency strategy is to switch to a preset passive safety damping mode, where all magnetorheological dampers maintain a fixed damping value, which is the reference damping value under the flat road scenario.

[0015] This invention provides an on-board magnetorheological active suspension damping matching control system. It has the following beneficial effects: 1. This invention collects multi-dimensional parameters such as road surface condition, vehicle posture, and vehicle operation through a multi-source sensing module. The central control module performs weighted fusion processing to eliminate signal interference and measurement errors. Combined with a pre-stored damping matching strategy library and an adaptive learning algorithm, it achieves precise decision-making for damping adjustment. With the stepless adjustment characteristics of the magnetorheological execution module, it can dynamically optimize the damping force in real time according to changes in driving state, effectively reducing vehicle posture fluctuations and improving vehicle ride comfort and handling stability.

[0016] 2. This invention enhances the system's scene adaptability and versatility. Relying on the scene recognition function of the adaptive learning algorithm, it can accurately match the damping adjustment requirements of different driving scenarios. Moreover, the strategy optimization unit can continuously update the damping matching rules based on the vehicle's cumulative driving data, achieving stable adaptation without the need for extensive debugging for specific vehicle models or road conditions. At the same time, the modular system architecture design makes each functional module relatively independent, which facilitates flexible integration and expansion according to the needs of different vehicle models, reducing the overall vehicle adaptation and maintenance costs.

[0017] 3. This invention ensures the stable operation of the magnetorheological actuator module through independent drive circuit and constant current control design. Combined with the multi-specification voltage output of the power supply module and the redundant design of the backup power supply, it can avoid the impact of vehicle power fluctuations on system operation. The added fault diagnosis module can monitor the working status of each module in real time and output corresponding emergency damping adjustment strategies for different fault levels to prevent system failure from causing sudden changes in suspension performance and provide continuous and reliable suspension support during driving. Attached Figure Description

[0018] Figure 1 This is the overall flowchart of the present invention; Figure 2 This is a flowchart of the data fusion processing of the present invention; Figure 3 This is a flowchart of the fault diagnosis and emergency handling process of the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see the appendix Figure 1 -Appendix Figure 3This invention provides an on-board magnetorheological active suspension damping matching control system, including a sensing module, a central control module, a magnetorheological execution module, and a power supply module. The sensing module and the central control module are bidirectionally electrically connected, and the signal output terminal of the central control module is electrically connected to the signal input terminal of the magnetorheological execution module. The power supply module is electrically connected to the sensing module, the central control module, and the magnetorheological execution module respectively and provides working power. The sensing module is used to collect road surface state parameters, vehicle attitude parameters, and vehicle operating parameters during vehicle driving. The central control module has a built-in damping matching strategy library and an adaptive learning algorithm, which is used to fuse and analyze the parameters collected by the sensing module and generate a target damping adjustment signal. The magnetorheological execution module is used to respond to the target damping adjustment signal to realize stepless adjustment of the damping force. The damping matching strategy library pre-stores damping reference parameters and dynamic correction rules under different driving scenarios.

[0021] Specifically, the power supply module provides stable power to each module, the sensing module comprehensively collects road conditions, vehicle posture and vehicle operating parameters and transmits them to the central control module; the central control module, relying on the built-in damping matching strategy library and adaptive learning algorithm, generates a target damping adjustment signal adapted to the current driving scenario after fusing and analyzing the multi-source parameters; the magnetorheological execution module responds to the signal and uses the magnetic control characteristics of magnetorheological materials to achieve stepless adjustment of damping force, ultimately achieving dynamic optimization of suspension performance.

[0022] The perception module includes a forward-looking perception unit, a vehicle body status perception unit, and an operating parameter perception unit. The forward-looking perception unit includes a lidar and a high-definition camera, both installed at the front of the vehicle's front compartment, used to collect data on road surface undulations and obstacle distribution within a range of 5-100 meters ahead. The vehicle body status perception unit includes a three-axis acceleration sensor installed at the vehicle's center of gravity, displacement sensors installed on the tops of each suspension tower, and an angular velocity sensor installed on the steering knuckle, used to collect data on the vehicle's vertical acceleration, pitch acceleration, roll acceleration, suspension compression, and wheel steering angular velocity. The operating parameter perception unit includes a vehicle speed sensor, wheel speed sensors, throttle opening sensor, and brake pressure sensor connected to the vehicle's CAN bus, used to collect real-time vehicle speed, wheel speeds, power output status, and braking status.

[0023] Specifically, data is collected collaboratively by the pre-aiming perception unit, the vehicle body state perception unit, and the operating parameter perception unit. The lidar and high-definition camera of the pre-aiming perception unit are installed at the front of the vehicle's front compartment to collect data on road surface undulations and obstacle distribution. The three-axis accelerometer, displacement sensor, and angular velocity sensor of the vehicle body state perception unit are installed at the vehicle's center of gravity, suspension tower top, and steering knuckle, respectively, to collect vehicle attitude-related parameters. Various sensors of the operating parameter perception unit communicate through the vehicle's CAN bus to collect vehicle operating parameters such as vehicle speed and wheel speed. The data from all units is aggregated to provide comprehensive support for subsequent control decisions.

[0024] The central control module includes a data fusion processing unit, a damping decision unit, and a strategy optimization unit. The data fusion processing unit performs spatiotemporal synchronous calibration and noise filtering on the multi-source parameters collected by the sensing module, and eliminates road interference signals and sensor measurement errors through a weighted Kalman filter algorithm. The damping decision unit calls the damping matching strategy library, calculates the initial damping value corresponding to the current driving scenario based on the fused parameters, and then dynamically corrects it based on historical adjustment data and real-time vehicle response through an adaptive learning algorithm to generate the final target damping adjustment signal. The strategy optimization unit updates the correction rules in the damping matching strategy library based on the vehicle's cumulative driving data.

[0025] Specifically, the data fusion processing unit performs spatiotemporal synchronization calibration and noise filtering on the multi-source parameters collected by the sensing module, and eliminates interference and measurement errors through a weighted Kalman filter algorithm; the damping decision unit calls the damping matching strategy library, calculates the initial damping value by combining the fused parameters, and then dynamically corrects it through an adaptive learning algorithm to generate the final target damping adjustment signal; the strategy optimization unit updates the correction rules in the strategy library based on the vehicle's cumulative driving data to ensure the accuracy and adaptability of the decision.

[0026] The magnetorheological actuator module includes four independent magnetorheological dampers, which are respectively installed in the suspension system of the four wheels of the vehicle. Each magnetorheological damper includes a cylinder, piston rod, magnetorheological fluid chamber, electromagnetic coil, and displacement feedback sensor. The magnetorheological fluid chamber contains a customized magnetorheological fluid. The electromagnetic coil is wound in the coil groove in the middle of the cylinder. The input end of the electromagnetic coil is electrically connected to the signal output end of the central control module. The displacement feedback sensor is installed at the connection between the piston rod and the cylinder to collect the real-time extension and contraction of the damper and feed it back to the central control module. The customized magnetorheological fluid is made of nano-scale soft iron particles, silicone oil carrier, and anti-settling additives. It can achieve continuous viscosity change of 10-1000 mPa·s within the magnetic field strength range of 0-2T.

[0027] Specifically, four independent magnetorheological dampers are installed on the vehicle's four-wheel suspension system. Each damper consists of a cylinder, piston rod, and other components forming its core structure, with a customized magnetorheological fluid filling the magnetorheological fluid chamber. An electromagnetic coil is wound around a coil slot in the cylinder and electrically connected to the central control module. Upon receiving a target damping adjustment signal, it generates a corresponding magnetic field. Under the influence of this magnetic field, the viscosity of the customized magnetorheological fluid changes continuously, thereby adjusting the damping force of the damper. A displacement feedback sensor collects the real-time extension and contraction of the damper and feeds it back to the central control module, forming an adjustment closed loop.

[0028] The adaptive learning algorithm includes a scene recognition sub-algorithm and a parameter optimization sub-algorithm. The scene recognition sub-algorithm classifies the fused driving parameters using a support vector machine model, identifying five typical driving scenarios: smooth roads, continuous bumps, rapid acceleration, rapid braking, and sharp turns. The parameter optimization sub-algorithm is based on the particle swarm optimization algorithm, with the minimum root mean square value of the vehicle's vertical acceleration and the minimum fluctuation of tire ground pressure as dual objective functions. It iteratively optimizes the initial damping value, and the optimization step size is dynamically adjusted according to the scene change rate. When the scene change rate is greater than 0.5 m / s, the optimization step size is shortened to 0.01 N·s / mm.

[0029] Specifically, the scene recognition sub-algorithm and the parameter optimization sub-algorithm work together to achieve precise adaptation of damping adjustment parameters. The scene recognition sub-algorithm uses a support vector machine model to classify the fused driving parameters, thereby identifying five typical driving scenarios, such as smooth roads and continuous bumpy roads. The parameter optimization sub-algorithm is based on the particle swarm optimization algorithm, with the minimum root mean square value of the vehicle's vertical acceleration and the minimum fluctuation of tire ground pressure as dual objective functions. It iteratively optimizes the initial damping value, and the optimization step size is dynamically adjusted according to the rate of change of the scenario to ensure the optimization accuracy under different rate of change of the scenario.

[0030] The data fusion processing unit's processing flow includes: First, aligning the timestamps of parameters collected by each sensor to ensure that the data acquisition time difference does not exceed 1ms; Second, using an amplitude-limiting filter to remove abnormal data that exceeds a reasonable range, which is determined by the measurement range of each sensor and the vehicle's dynamic characteristics; Third, fusing data from different sensors of the same source using a weighted Kalman filter algorithm, with the weighting coefficients dynamically allocated according to the sensor's measurement accuracy. The weighting coefficient for road surface undulation data from the lidar is 0.6, and the weighting coefficient for road surface undulation data from the high-definition camera is 0.4; Fourth, outputting the fused unified parameter set to the damping decision unit.

[0031] Specifically, a four-step progressive process is used to achieve precise integration of multi-source parameters. First, the timestamps of each sensor parameter are aligned to ensure data acquisition synchronization. Then, an amplitude-limiting filter is used to remove abnormal data that exceeds a reasonable range, which is determined by the sensor measurement range and vehicle dynamics characteristics. Next, a weighted Kalman filter algorithm is used to fuse data from different sensors of the same source, dynamically allocating weight coefficients based on measurement accuracy. Finally, a unified parameter set is output to the damping decision unit, providing a reliable data foundation for subsequent decisions. Precise adaptation of damping adjustment parameters is achieved through the collaborative use of scene recognition and parameter optimization sub-algorithms. The scene recognition sub-algorithm uses a support vector machine model to classify the fused driving parameters, thereby identifying five typical driving scenarios, including smooth roads and continuous bumps. The parameter optimization sub-algorithm, based on particle swarm optimization, uses the minimum root mean square value of vehicle vertical acceleration and the minimum fluctuation of tire ground pressure as dual objective functions to iteratively optimize the initial damping value. The optimization step size is dynamically adjusted according to the scene change rate to ensure optimization accuracy under different scene change rates.

[0032] Each magnetorheological damper is equipped with an independent drive circuit, which includes a D / A conversion module, a power amplifier module, and a constant current control module. The D / A conversion module converts the digital damping adjustment signal output from the central control module into an analog voltage signal. The power amplifier module amplifies the analog voltage signal to a drive voltage range of 0-12V. The constant current control module converts the drive voltage into a constant current output of 0-5A to the electromagnetic coil, ensuring that the stability error of the magnetic field strength generated by the electromagnetic coil does not exceed ±2%. The power supply module includes a vehicle-mounted 12V power supply, a DC / DC converter, and a backup power supply. The DC / DC converter converts the voltage of the vehicle-mounted 12V power supply into stable voltages of 5V, 12V, and 24V, respectively powering the sensing module, the central control module, and the magnetorheological actuator module. The backup power supply is a supercapacitor bank. When the voltage of the vehicle-mounted 12V power supply drops below 10V, the backup power supply automatically starts working, ensuring continuous system operation for at least 10 seconds. The supercapacitor bank has a capacity of at least 500F.

[0033] Specifically, each magnetorheological damper is equipped with an independent drive circuit that ensures magnetic field stability through multi-module collaboration. The D / A conversion module converts the digital damping adjustment signal output from the central control module into an analog voltage signal. The power amplification module amplifies this analog voltage signal to the corresponding drive voltage range. The constant current control module then converts the drive voltage into a constant current output to the electromagnetic coil, ensuring the stability of the magnetic field strength generated by the electromagnetic coil. Based on the vehicle's 12V power supply, a DC / DC converter converts it into three stable voltage specifications to power the sensing module, the central control module, and the magnetorheological actuator module. A backup supercapacitor bank automatically activates when the vehicle's 12V power supply voltage drops below a set value, ensuring continuous power supply to the system and guaranteeing operational stability. It also includes a fault diagnosis module, which is bidirectionally electrically connected to the central control module. The fault diagnosis module monitors the real-time operating status of the sensing module, central control module, and magnetorheological actuator module, collecting the power supply voltage, operating current, and signal transmission status of each module. When an abnormal state is detected, a fault code is generated and fed back to the central control module. The central control module outputs a corresponding emergency damping adjustment signal according to the fault level and simultaneously sends the fault code to the vehicle's instrument panel for display. The fault levels are divided into three levels: Level 1 faults are sensor signal drift, and the corresponding emergency strategy is to replace the current abnormal sensor data with the historical average and maintain a stable damping value; Level 2 faults are the failure of a single magnetorheological damper, and the corresponding emergency strategy is to adjust the damping value of the other magnetorheological damper on the same side to a safe reference value while reducing the vehicle's maximum permissible speed; Level 3 faults are the partial failure of the central control module, and the corresponding emergency strategy is to switch to a preset passive safety damping mode, where all magnetorheological dampers maintain a fixed damping value, which is the reference damping value for a smooth road scenario.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A vehicle-mounted magnetorheological active suspension damping matching control system, characterized in that, The system includes a sensing module, a central control module, a magnetorheological (MR) actuator module, and a power supply module. The sensing module and the central control module are bidirectionally electrically connected, and the signal output terminal of the central control module is electrically connected to the signal input terminal of the MR actuator module. The power supply module is electrically connected to the sensing module, the central control module, and the MR actuator module, respectively, and provides them with operating power. The sensing module is used to collect road surface state parameters, vehicle posture parameters, and vehicle operating parameters during vehicle operation. The central control module has a built-in damping matching strategy library and an adaptive learning algorithm, which is used to fuse and analyze the parameters collected by the sensing module and generate a target damping adjustment signal. The MR actuator module is used to respond to the target damping adjustment signal to achieve stepless adjustment of the damping force. The damping matching strategy library pre-stores damping reference parameters and dynamic correction rules for different driving scenarios.

2. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 1, characterized in that, The perception module includes a pre-aiming perception unit, a vehicle body status perception unit, and an operating parameter perception unit. The pre-aiming perception unit includes a lidar and a high-definition camera. Both the lidar and the high-definition camera are installed at the front of the vehicle's front compartment and are used to collect road surface undulation data and obstacle distribution data within a range of 5-100 meters ahead. The vehicle body status sensing unit includes a three-axis acceleration sensor installed at the vehicle's center of gravity, displacement sensors installed on the tops of each suspension tower, and an angular velocity sensor installed on the steering knuckle, used to collect the vehicle's vertical acceleration, pitch acceleration, roll acceleration, suspension compression, and wheel steering angular velocity; the operating parameter sensing unit includes a vehicle speed sensor, wheel speed sensors, throttle opening sensor, and brake pressure sensor that are connected to the vehicle's CAN bus, used to collect real-time vehicle speed, wheel speed, power output status, and braking status.

3. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 1, characterized in that, The central control module includes a data fusion processing unit, a damping decision unit, and a strategy optimization unit. The data fusion processing unit performs spatiotemporal synchronous calibration and noise filtering on the multi-source parameters collected by the sensing module, and eliminates road interference signals and sensor measurement errors through a weighted Kalman filter algorithm. The damping decision unit calls the damping matching strategy library, calculates the initial damping value corresponding to the current driving scenario based on the fused parameters, and then dynamically corrects it based on historical adjustment data and real-time vehicle response through the adaptive learning algorithm to generate the final target damping adjustment signal. The strategy optimization unit is used to update the correction rules in the damping matching strategy library based on the vehicle's cumulative driving data.

4. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 1, characterized in that, The magnetorheological actuation module includes four independent magnetorheological dampers, which are respectively installed in the suspension system of the four wheels of the vehicle. Each magnetorheological damper includes a cylinder, a piston rod, a magnetorheological fluid chamber, an electromagnetic coil, and a displacement feedback sensor. The magnetorheological fluid chamber contains a customized magnetorheological fluid. The electromagnetic coil is wound in a coil groove in the middle of the cylinder. The input end of the electromagnetic coil is electrically connected to the signal output end of the central control module. The displacement feedback sensor is installed at the connection between the piston rod and the cylinder to collect the real-time extension and contraction of the damper and feed it back to the central control module. The customized magnetorheological fluid is made of nano-sized soft iron particles, silicone oil carrier, and anti-settling additives. It can achieve continuous viscosity variation of 10-1000 mPa·s within the magnetic field strength range of 0-2T.

5. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 1, characterized in that, The adaptive learning algorithm includes a scene recognition sub-algorithm and a parameter optimization sub-algorithm. The scene recognition sub-algorithm classifies the fused driving parameters using a support vector machine model, identifying five typical driving scenarios: smooth roads, continuous bumps, rapid acceleration, rapid braking, and sharp turns. The parameter optimization sub-algorithm is based on the particle swarm optimization algorithm, with the minimum root mean square value of the vehicle's vertical acceleration and the minimum fluctuation of tire ground pressure as dual objective functions. It iteratively optimizes the initial damping value, and the optimization step size is dynamically adjusted according to the scene change rate. When the scene change rate is greater than 0.5 m / s, the optimization step size is shortened to 0.01 N·s / mm.

6. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 3, characterized in that, The data fusion processing unit's processing flow includes: First, aligning the timestamps of parameters collected by each sensor to ensure that the data acquisition time difference does not exceed 1ms; Second, using an amplitude-limiting filter to remove abnormal data that exceeds a reasonable range, where the reasonable range is determined by the measurement range of each sensor and the vehicle's dynamic characteristics; Third, fusing data from different sensors of the same source using a weighted Kalman filter algorithm, with the weighting coefficients dynamically allocated according to the sensor's measurement accuracy, the weighting coefficient for road surface undulation data from the lidar being 0.6 and the weighting coefficient for road surface undulation data from the high-definition camera being 0.4; Fourth, outputting the fused unified parameter set to the damping decision unit.

7. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 4, characterized in that, Each magnetorheological damper is equipped with an independent drive circuit, which includes a D / A conversion module, a power amplification module, and a constant current control module. The D / A conversion module converts the digital damping adjustment signal output by the central control module into an analog voltage signal. The power amplification module amplifies the analog voltage signal to a drive voltage range of 0-12V. The constant current control module converts the drive voltage into a constant current of 0-5A and outputs it to the electromagnetic coil, ensuring that the stability error of the magnetic field strength generated by the electromagnetic coil does not exceed ±2%.

8. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 1, characterized in that, The power supply module includes a vehicle-mounted 12V power supply, a DC / DC converter, and a backup power supply. The DC / DC converter converts the voltage of the vehicle-mounted 12V power supply into stable voltages of 5V, 12V, and 24V, which power the sensing module, central control module, and magnetorheological actuator module, respectively. The backup power supply is a supercapacitor bank. When the voltage of the vehicle-mounted 12V power supply drops below 10V, the backup power supply automatically starts working to ensure continuous system operation for at least 10 seconds. The capacity of the supercapacitor bank is at least 500F.

9. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 1, characterized in that, It also includes a fault diagnosis module, which is bidirectionally electrically connected to the central control module. The fault diagnosis module is used to monitor the working status of the sensing module, the central control module, and the magnetorheological actuator module in real time, and to collect the power supply voltage, operating current, and signal transmission status of each module. When an abnormal state is detected, a fault code is generated and fed back to the central control module. The central control module outputs the corresponding emergency damping adjustment signal according to the fault level, and at the same time sends the fault code to the vehicle instrument panel for display.

10. The vehicle-mounted magnetorheological active suspension damping matching control system according to claim 9, characterized in that, The fault levels are divided into three levels. Level 1 faults are sensor signal drift, and the corresponding emergency strategy is to replace the current abnormal sensor data with the historical average value and maintain the damping value stable. Level 2 faults are failures of a single magnetorheological damper, and the corresponding emergency strategy is to adjust the damping value of the other magnetorheological damper on the same side to a safe reference value, while reducing the maximum permissible vehicle speed. Level 3 faults are partial failures of the central control module, and the corresponding emergency strategy is to switch to a preset passive safety damping mode, where all magnetorheological dampers maintain a fixed damping value, which is the reference damping value under the flat road scenario.

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