CN116749700A - Vehicle active suspension control method considering passenger motion based on road surface information - Google Patents
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Abstract
The invention relates to a vehicle active suspension control method considering passenger motion based on road surface information, which comprises the following steps: establishing a coupling dynamics model of the active suspension of the vehicle-seat; the method comprises the steps of identifying random pavement information in the running process of a vehicle by using a binocular vision identification algorithm, grading the pavement information, and inputting the pavement grade information into a vehicle-chair active suspension system model by using a pre-aiming control algorithm to serve as excitation signal input; the method comprises the steps of performing rolling optimization on an active suspension performance index by using a Model Predictive Control (MPC) algorithm, reducing the vertical acceleration value of an active suspension system, fully considering the influence of vertical vibration of a vehicle suspension caused by vertical acceleration of the vehicle to cause motion sickness of passengers, respectively controlling the researched key performance indexes, improving the vertical acceleration of the vehicle by improving the algorithm, finally effectively improving the smoothness and the operation stability of the automatic driving vehicle, and reducing the occurrence rate of the motion sickness of the passengers in the vehicle.
Description
Technical Field
The invention relates to the technical field of intelligent control of automatic driving automobiles, in particular to a vehicle active suspension control method considering passenger motion based on road surface information.
Background
At present, with the development of intelligent vehicle technology, the automobile automatic driving technology is greatly improved, and the riding comfort and the steering stability of the automatic driving vehicle are directly related to the popularization and the application of the automatic driving technology. Because the roles of drivers are changed in the running process of the automatic driving vehicle, passengers in the vehicle generally perform a series of leisure activities in the running process of the vehicle, however, the occurrence of motion sickness of the passengers in the running process of the vehicle can cause uncomfortable bodies of the passengers, and the riding experience is poor. Therefore, how to improve the running stability of the automatic driving vehicle and the smoothness of the vehicle can effectively improve the carsickness effect of passengers and reduce the carsickness rate, which is a problem to be solved in the automatic driving industry at present.
The development of the drive-by-wire chassis technology brings possibility for improving the smoothness and the operation stability of the automatic driving vehicle, and the drive-by-wire chassis technology is continuously popularized at present and becomes the standard of more and more medium-high-grade automobiles. Based on the characteristics of the drive-by-wire chassis, the vehicle active suspension technology is also widely applied, and the active suspension can adaptively adjust the damping characteristic of the suspension based on the input of road surface information, so that the smoothness of the vehicle is improved. The occurrence of passenger motion sickness is affected by the lateral and longitudinal acceleration of the vehicle as well as the vertical acceleration of the vehicle. If the vertical acceleration of the vehicle can be reduced during the running process of the vehicle, the passenger carsickness rate can be effectively reduced, so the use of the active suspension plays an important role in improving the technology of the automatic driving vehicle.
In the field of intelligent automobiles which are rapidly developed, vehicle-mounted sensors such as laser radars, binocular cameras and the like are used for providing more abundant perception information for automobiles. The development of the perception technology enables the intelligent automobile to have 'eyes' belonging to the intelligent automobile, surrounding things can be accurately judged, and the application of the perception technology plays an irreplaceable role in the development of the automatic driving automobile. The vehicle driving road surface unevenness information perceived by the visual recognition technology is added into the design of the active suspension optimization algorithm, so that the visual perception can be fully utilized to realize the pre-aiming control of the suspension system, the smoothness and the operation stability of the vehicle are effectively improved, and the comfort of the vehicle is further improved. At present, the intelligent vehicle cannot improve smoothness in the running process of the vehicle in real time based on road surface characteristics, so that driving comfort of the vehicle is further improved, and probability of occurrence of motion sickness of passengers in the vehicle is increased.
Disclosure of Invention
The invention aims to provide a vehicle active suspension control method considering motion of passengers based on road surface information, aiming at the problem that passengers are easy to motion sickness in the driving process of an automatic driving automobile, the road surface information is identified by using a binocular vision identification technology and is input into a vehicle-chair active suspension system, and a vehicle-mounted controller can timely adjust the damping characteristic of the vehicle-chair active suspension system according to a road surface unevenness signal, so that the vehicle can have optimal comfort when driving on a corresponding road surface.
The technical scheme adopted by the invention is as follows:
the invention provides a vehicle active suspension control method considering passenger motion based on road surface information, which comprises the following steps:
s1, establishing a dynamic model of a vehicle-seat active suspension system; the damping size of the CDC shock absorber in the active suspension can be adjusted in real time along with the difference of road surface information by analyzing the model under the input of the road surface information;
s2, extracting road surface information above the front side of the vehicle by using a binocular camera, identifying random road surface information in the running process of the vehicle by using a binocular visual identification algorithm, grading the road surface information, and inputting the road surface grade information into a vehicle-seat active suspension system model by using a pre-aiming control algorithm to serve as an excitation signal to be input;
s3, a motion state of the vehicle is perceived by the motion sickness model system, and the motion sickness model system comprises a motion excitation signal of the vehicle and an excitation signal of a seat suspension to the body of an occupant;
and S4, performing rolling optimization on the performance index of the active suspension by using a Model Predictive Control (MPC) algorithm, reducing the vertical acceleration value of the active suspension system, fully considering the influence of vertical vibration of the vehicle caused by vertical acceleration of the suspension of the vehicle to cause motion sickness of passengers, respectively controlling key performance indexes, improving the vertical acceleration of the vehicle by improving the algorithm, and finally effectively improving the smoothness and the steering stability of the automatic driving vehicle.
Further, in the step S1, a differential equation of the dynamic model of the vehicle-seat active suspension system is as follows:
wherein: m is m d Is the unsprung mass, m c Is the sprung mass, m b Total mass of human and vehicle seats, z g For input displacement of road surface, z b Z is the displacement of the seat c For displacement of the body, z d For displacement of tyre, k b 、k c 、k d Damping coefficients of the corresponding springs are respectively shown, and F1 and F2 are control forces;
writing the differential equation as a state space equation can be given as follows:
obtaining a state vector z (t), a state matrix A, a control input U (t), a control input matrix B, a noise input matrix F and Gaussian white noise W (t);
U(t)＝[F 1 F 2 ] T ，W(t)＝[w t ]。
further, the step S4 specifically includes: performing MPC algorithm design on the dynamic model of the vehicle-seat active suspension system obtained in the step S1, predicting the future state of a controlled object by adopting a discrete model through the MPC algorithm, obtaining the optimal control quantity by solving the optimization problem in a limited domain, and discretizing the continuous vehicle active suspension dynamic equation:
wherein:t is the control step length; x (k|k), ω (k|k) is a measurement quantity at the kth time, representing the real system state and road surface excitation at the K time; y (k|k), u (k|k) and x (k+ 1|k) are predicted quantities at time k, representing the system output at time k predicted at time k, the control quantity at time k and the system state at time k+1;
furthermore, in order to ensure that the vehicle has good smoothness and steering stability, the objective function of the MPC controller optimization problem is set to be min J (y, u), namely the output and damping force of the system are as small as possible, and the impact of road surface excitation on a human body is reduced;
wherein: q and R are weight matrices, respectively.
Further, in the step S2, during the process of extracting the road information by the binocular camera, a database is built by using a large number of pictures containing different road information, based on the VGGNet structure, the whole network uses the convolution kernel with the same size and the maximum pooling size, and the VGG16 neural network is used for adding labels to the pictures in the database and performing training learning; according to different international standardization time domain disturbance curves under different road conditions, describing the road surface PSD; the following formula is typically used to fit the power spectrum of the road excitation:
wherein: spatial frequency of n (m -1 ) The method comprises the steps of carrying out a first treatment on the surface of the The reference spatial frequency is n 0 The method comprises the steps of carrying out a first treatment on the surface of the Generally take n 0 ＝0.1(m -1 ) The method comprises the steps of carrying out a first treatment on the surface of the Road surface unevenness coefficient G q (n 0 )(m 3 ) The frequency index is typically chosen to be 2.
Further, the carsickness model system comprises an occupant body excitation sensing module, a vehicle state sensing module and a user riding comfort feedback module; the vehicle state sensing module is used for sensing the motion state of the vehicle, and comprises a motion excitation signal of the vehicle; the passenger body excitation sensing module is used for sensing excitation signals of the seat suspension to the passenger body; the user riding comfort feedback module is used for adjusting the running mode of the vehicle according to subjective feeling of passengers and timely switching the running mode of the suspension system of the vehicle.
Compared with the prior art, the invention has the following beneficial effects:
aiming at the situation that the existing intelligent vehicle cannot improve the smoothness in the running process of the vehicle in real time through the road surface information in the running process of the vehicle, the driving comfort of the vehicle is improved. Compared with the traditional method for improving the vehicle smoothness, the method combines real-time road surface information and optimizes the vertical acceleration of the active suspension as an optimization target in real time on the basis of improving the vehicle smoothness, and effectively improves the actual riding comfort of passengers in the vehicle. Most of the previous inventions were designed based on pre-aiming of the front axle of the vehicle and vibration damping of the rear axle. According to the invention, the front road surface information is directly extracted by a visual recognition technology and is used as target input, and the optimal vibration reduction effect can be achieved after optimization, so that the occurrence probability of the motion sickness of the passengers is reduced.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention;
FIG. 2 is a schematic illustration of the operation of the active suspension system of the car-seat of the present invention;
FIG. 3 is a schematic diagram of a binocular camera capturing road surface information;
FIG. 4 is a schematic diagram of different levels of road surface excitation signals;
figure 5 motion sickness produces a schematic.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The invention provides a vehicle active suspension control method considering passenger motion based on road surface information, which is shown in figures 1-5 and comprises the following specific implementation processes:
s1, establishing a dynamic model of a vehicle-seat active suspension system; the vehicle-seat active suspension system comprises a vehicle body mass module, a variable damping shock absorber (CDC), an unsprung mass module, a sprung mass module, a sensor, an ECU (electronic control unit), a driving motor, an air spring and the like;
the differential equation of the vehicle-seat active suspension system dynamics model is as follows:
wherein: m is m d Is the unsprung mass, m c Is the sprung mass, m b Total mass of human and vehicle seats, z g For input displacement of road surface, z b Z is the displacement of the seat c For displacement of the body, z d For displacement of tyre, k b 、k c 、k d Damping coefficients of the corresponding springs are respectively shown, and F1 and F2 are control forces;
writing the differential equation as a state space equation can be given as follows:
obtaining a state vector z (t), a state matrix A, a control input U (t), a control input matrix B, a noise input matrix F and Gaussian white noise W (t);
U(t)＝[F 1 F 2 ] T ，W(t)＝[w t ]。
according to the invention, newton's mechanical law is used for establishing a differential equation of a dynamic model of the vehicle-seat active suspension system, and the differential equation is converted into a state space equation in order to facilitate the design of a controller. The corresponding model predictive controller is designed by discretizing a state space equation, key parameters such as control step length, predictive step length and the like are designed, and finally, the control target of the vehicle-seat active suspension system is subjected to rolling optimization, wherein the vertical acceleration of the vehicle body is mainly reduced as a main performance index.
By analyzing the model, the damping size of the CDC shock absorber in the active suspension can be adjusted in real time according to different road surface information under the condition that the road surface information is input. If the vehicle passes through a deceleration strip or a pothole road section, the vehicle can resume the running state of the vehicle in the shortest time by suspending the vehicle, and when the vehicle runs on a long slope or a road section with a large curvature, the peak value of the lateral and longitudinal acceleration of the vehicle is reduced by controlling the sideslip force of the wheels or the like through the chassis of the vehicle. The reason for the motion sickness of the passengers is that the lateral and longitudinal acceleration changes greatly during the running of the vehicle, and the acceleration frequency is in a certain range. The vehicle is more stable through the action of the active suspension, and the motion sickness rate can be effectively reduced.
S2, extracting road surface information above the front side of the vehicle by using a binocular camera, identifying random road surface information in the running process of the vehicle by using a binocular visual identification algorithm, grading the road surface information, and inputting the road surface grade information into a vehicle-seat active suspension system model by using a pre-aiming control algorithm to serve as an excitation signal to be input;
in the process of extracting road surface information by a binocular camera, constructing a database by utilizing a large number of pictures containing different road surface information, based on a VGGNet structure, using a convolution kernel with the same size and the maximum pooling size in the whole network, adding labels into the pictures in the database by utilizing a VGG16 neural network, and training and learning; according to different international standardization time domain disturbance curves under different road conditions, describing the road surface PSD; the following formula is typically used to fit the power spectrum of the road excitation:
wherein: spatial frequency of n (m -1 ) The method comprises the steps of carrying out a first treatment on the surface of the The reference spatial frequency is n 0 The method comprises the steps of carrying out a first treatment on the surface of the Generally take n 0 ＝0.1(m -1 ). Road surface unevenness coefficient G q (n 0 )(m 3 ) The frequency index is typically chosen to be 2.
The standard GB-7031-1986 divides the road surface unevenness coefficient into 8 grades A-H, wherein the grade A road surface is a corresponding expressway and the road surface condition thereof, so the grade A road surface has the best vehicle passing performance, the grade E road surface is an unpaved road surface, the grade H road surface is the worst road surface condition, and the vehicle passing performance is poor. In the training and extracting process of road information, road pictures under different labels are subjected to clustering analysis, and according to the conditions of various roads, the road pictures are classified into 8 types of roads according to national grade standards, and the road conditions are the best to the worst in sequence. The road surface excitation signals are input into the vehicle-seat active suspension system as system input information by comparing the road surface information with different road surface excitation signals through the road surface information with different classification grades.
The method has the advantages that through the image recognition technology, the binocular camera on the vehicle is utilized to extract data of the ground which is about to pass through the vehicle, the grade of the vehicle passing through the road surface is recognized in real time, road surface grade information is transmitted to the vehicle-mounted controller in real time, the controller sends instruction information, the rigidity of the suspension system is adjusted in real time, a pre-aiming control strategy in vehicle suspension control is formed, and the smoothness and the comfort of the vehicle are effectively improved. The controller is combined with information such as road identification results, vehicle speed, distance and the like to calculate the actuation time of the system, so that the system can actuate at more accurate time, suspension is increased and reduced, damping is increased or reduced, and further better comfort and trafficability are obtained.
And training and learning various types of pictures in the road picture data set by using an image recognition algorithm, and improving the accuracy of algorithm recognition by using a VGG-16 algorithm. After a series of early training learning, the algorithm has more accurate recognition degree on the road types of the vehicles, different types of road surfaces on which the vehicles run are recognized by using the vehicle-mounted binocular cameras, the road surface information of different grades is subjected to cluster analysis, all the road surfaces are classified according to corresponding grades, and the road surface grades of different types are input to the vehicle-mounted controller; in the visual identification process, if the identified road surface grade is grade A road surface information, the grade A road surface information is input into the vehicle-seat active suspension system in real time, so that a suspension pre-aiming control strategy framework is formed. The ECU of the active suspension of the automatic driving vehicle can control the vehicle system to adjust the damping rigidity of the suspension in real time on line according to the road surface input information according to priori experience after receiving the road surface excitation signal, thereby better passing through the running road surface and improving the running comfort and smoothness of the automatic driving vehicle.
S3, a motion state of the vehicle is perceived by the motion sickness model system, and the motion sickness model system comprises a motion excitation signal of the vehicle and an excitation signal of a seat suspension to the body of an occupant; the carsickness model system mainly comprises an occupant body excitation sensing module, a vehicle state sensing module and a user riding comfort feedback module; the vehicle state sensing module is used for sensing the motion state of the vehicle, and comprises a motion excitation signal of the vehicle; the passenger body excitation sensing module is used for sensing excitation signals of the seat suspension to the passenger body; the user riding comfort feedback module is used for adjusting the running mode of the vehicle according to subjective feeling of passengers and timely switching the running mode of the suspension system of the vehicle.
Because the motion sickness of the passengers is mainly caused by subjective vertical conflict, the subjective vertical conflict model (SVC) mainly considers the passive motion situation of the passengers, and the influence of human brain vestibule function, visual stimulus and other factors on the motion sickness is ignored. If the longitudinal and transverse movements of the vehicle are unstable during running, the vehicle is influenced by the road surface input excitation signals during the vertical movement, so that the passengers can be in motion. If the human body is in a vibrating environment for a long time, the motion sickness symptoms are gradually aggravated, and the probability of vomiting is increased. Although the sensitivity of the human body to horizontal motion is slightly higher than in the vertical and pitch directions, the contribution rate of motion sickness mainly results from vibration in the horizontal direction, but the occurrence of motion sickness due to vertical vibration also has a large specific gravity. In the invention, the input of the vertical excitation signal mainly considers how to reduce the occurrence of motion sickness symptoms of passengers because the vertical excitation frequency of passengers is in the range of motion sickness occurrence. Here, the motion sickness dose value MSDV is mainly used as an index of motion sickness occurrence, and the frequency with the greatest influence on MSDV, that is, the highest weight, is approximately 0.16Hz.
Wherein: a, a w Representing vibration weighted acceleration, T is not time limited.
In the running process of the vehicle, road surface excitation signals are continuously transmitted to passengers through the vehicle body, on the basis of self-adaptive damping characteristic adjustment of the vehicle-chair active suspension system, the passengers can select suspension system operation models through a user riding comfort feedback module according to subjective riding feeling of the individual, such as: the degree of softness of the suspension, etc. If the passenger motion sickness dose value is larger than the general level through the calculation of the passenger body excitation sensing module, the riding comfort of the passenger is poor, and the MSDV value is higher. The vehicle driving mode should be adjusted in time through the vehicle body state sensing module, so that the occurrence of the motion sickness of the passengers is slowed down.
In the running process of an automatic driving vehicle, an occupant sits on a seat in the vehicle under the general condition, the body of the occupant continuously receives excitation signals transmitted by wheels, and finally the excitation signals are subjected to sensing processing through a human brain vestibule system and the like. If the transmission frequency of the excitation signal is in the most sensitive range of the human body, the phenomena of uncomfortable human body, even vomit and the like can be caused, so that riding experience of passengers is poor. To overcome this problem, the occupant can switch the driving mode of the vehicle on the fly according to his subjective feeling, and adjust the damping characteristics of the vehicle-seat active suspension system on the fly. The various sensing modules in the vehicle can also calculate whether the motion sickness occurrence dosage value exceeds the human body normal bearing value through a series of transmission signals, if so, the motion sickness occurrence dosage value is fed back to the vehicle controller through the state sensing module of the vehicle, the vehicle further adjusts the running state of the vehicle, and the Motion Sickness Dosage Value (MSDV) is reduced.
S4, performing rolling optimization on the performance index of the active suspension by using a Model Predictive Control (MPC) algorithm, reducing the vertical acceleration value of an active suspension system, fully considering the influence of vertical vibration of the vehicle caused by vehicle vertical acceleration of the suspension of the vehicle to cause motion sickness of passengers, respectively controlling key performance indexes, improving the vertical acceleration of the vehicle by improving the algorithm, and finally effectively improving the smoothness and the steering stability of the automatic driving vehicle; the method specifically comprises the following steps:
performing MPC algorithm design on the dynamic model of the vehicle-seat active suspension system obtained in the step S1, predicting the future state of a controlled object by adopting a discrete model through the MPC algorithm, obtaining the optimal control quantity by solving the optimization problem in a limited domain, and discretizing the continuous vehicle active suspension dynamic equation:
wherein: a is that d ＝e AT ,T is the control step length; x (k|k), ω (k|k) is a measurement quantity at the kth time, representing the real system state and road surface excitation at the K time; y (k|k), u (k|k) and x (k+ 1|k) are predicted quantities at time k, representing the system output at time k predicted at time k, the control quantity at time k and the system state at time k+1;
in order to ensure that the vehicle has good smoothness and steering stability, setting an objective function of the MPC controller optimization problem as minJ (y, u), namely enabling the output and damping force of the system to be as small as possible, and reducing impact of road surface excitation on a human body;
wherein: q and R are weight matrices, respectively.
The invention is not fully described in detail in the prior art.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (6)
1. A vehicle active suspension control method that considers occupant motion based on road surface information, characterized by: which comprises the following steps:
s1, establishing a dynamic model of a vehicle-seat active suspension system; the damping size of the CDC shock absorber in the active suspension can be adjusted in real time along with the difference of road surface information by analyzing the model under the input of the road surface information;
s2, extracting road surface information above the front side of the vehicle by using a binocular camera, identifying random road surface information in the running process of the vehicle by using a binocular visual identification algorithm, grading the road surface information, and inputting the road surface grade information into a vehicle-seat active suspension system model by using a pre-aiming control algorithm to serve as an excitation signal to be input;
s3, a motion state of the vehicle is perceived by the motion sickness model system, and the motion sickness model system comprises a motion excitation signal of the vehicle and an excitation signal of a seat suspension to the body of an occupant;
and S4, performing rolling optimization on the performance index of the active suspension by using a Model Predictive Control (MPC) algorithm, reducing the vertical acceleration value of the active suspension system, fully considering the influence of vertical vibration of the vehicle caused by vertical acceleration of the suspension of the vehicle to cause motion sickness of passengers, respectively controlling key performance indexes, improving the vertical acceleration of the vehicle by improving the algorithm, and finally effectively improving the smoothness and the steering stability of the automatic driving vehicle.
2. The vehicle active suspension control method that considers occupant motion based on road surface information according to claim 1, characterized in that: in the step S1, the differential equation of the dynamic model of the vehicle-seat active suspension system is as follows:
wherein: m is m d Is the unsprung mass, m c Is the sprung mass, m b Total mass of human and vehicle seats, z g For input displacement of road surface, z b Z is the displacement of the seat c For displacement of the body, z d For displacement of tyre, k b 、k c 、k d Damping coefficients of the corresponding springs are respectively shown, and F1 and F2 are control forces;
writing the differential equation as a state space equation can be given as follows:
obtaining a state vector z (t), a state matrix A, a control input U (t), a control input matrix B, a noise input matrix F and Gaussian white noise W (t);
U(t)＝[F 1 F 2 ] T ，W(t)＝[w t ]。
3. the vehicle active suspension control method considering occupant motion based on road surface information according to claim 2, wherein the step S3 specifically includes: performing MPC algorithm design on the dynamic model of the vehicle-seat active suspension system obtained in the step S1, predicting the future state of a controlled object by adopting a discrete model through the MPC algorithm, obtaining the optimal control quantity by solving the optimization problem in a limited domain, and discretizing the continuous vehicle active suspension dynamic equation:
wherein: a is that d ＝e AT ,T is the control step length; x (k|k), ω (k|k) is a measurement quantity at the kth time, representing the real system state and road surface excitation at the K time; y (k|k), u (k|k) and x (k+ 1|k) are predicted quantities at time k, representing the system output at time k predicted at time k, the control quantity at time k and the system state at time k+1.
4. A vehicle active suspension control method that considers occupant motion based on road surface information according to claim 3, characterized in that: in order to ensure that the vehicle has good smoothness and steering stability, setting an objective function of the MPC controller optimization problem as minJ (y, u), namely enabling the output and damping force of the system to be as small as possible, and reducing impact of road surface excitation on a human body;
wherein: q and R are weight matrices, respectively.
5. The vehicle active suspension control method that considers occupant motion based on road surface information according to claim 2, characterized in that: in the step S2, in the process of extracting the road surface information by the binocular camera, a database is built by utilizing a large number of pictures containing different road surface information, based on a VGGNet structure, the whole network uses convolution kernels with the same size and the maximum pooling size, and a VGG16 neural network is utilized to add labels to the pictures in the database and perform training learning; according to different international standardization time domain disturbance curves under different road conditions, describing the road surface PSD; the following formula is typically used to fit the power spectrum of the road excitation:
wherein: spatial frequency of n (m -1 ) The method comprises the steps of carrying out a first treatment on the surface of the The reference spatial frequency is n 0 The method comprises the steps of carrying out a first treatment on the surface of the Generally take n 0 ＝0.1(m -1 ) The method comprises the steps of carrying out a first treatment on the surface of the Road surface unevenness coefficient G q (n 0 )(m 3 ) The frequency index is typically chosen to be 2.
6. The vehicle active suspension control method that considers occupant motion based on road surface information according to claim 2, characterized in that: the carsickness model system comprises an occupant body excitation sensing module, a vehicle state sensing module and a user riding comfort feedback module; the vehicle state sensing module is used for sensing the motion state of the vehicle, and comprises a motion excitation signal of the vehicle; the passenger body excitation sensing module is used for sensing excitation signals of the seat suspension to the passenger body; the user riding comfort feedback module is used for adjusting the running mode of the vehicle according to subjective feeling of passengers and timely switching the running mode of the suspension system of the vehicle.
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| CN117554097A (en) * | 2024-01-12 | 2024-02-13 | 山东鲁岳桥机械股份有限公司 | Intelligent monitoring device for vehicle suspension faults |
| CN117554097B (en) * | 2024-01-12 | 2024-04-02 | 山东鲁岳桥机械股份有限公司 | Intelligent monitoring device for vehicle suspension faults |
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