MR semi-active suspension system control method based on environment perception and prediction

Abstract

The invention discloses an MR semi-active suspension system control method based on environmental perception and prediction, comprising: a detection module comprising an MR damper temperature estimation module comprising a temperature sensor arranged on or near an MR damper; the control module comprises an MR damper temperature control module and a heating module arranged in the MR damper; when the suspension is in a steady state, estimating the temperature of the MR fluid through a heat balance equation; the suspension is under a dynamic condition, and the temperature of the MR fluid is estimated by estimating the change of the temperature along with time through numerical integration; the temperature of the MR fluid is judged, and if the temperature of the MR fluid is smaller than the normal temperature, the current conveyed to the heating module is increased for heating, so that the temperature of the MR fluid is increased; and if the temperature of the MR fluid is higher than the normal temperature, the current conveyed to the heating module is reduced, so that the temperature of the fluid in the four MR dampers of the vehicle can be controlled at the normal temperature, the performance is optimal, and the reliability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile suspension, and particularly relates to a MR semi-active suspension system control method based on environment sensing and prediction. BACKGROUND

[0002] In the prior art, the control strategy of the magneto-rheological semi-active suspension is fixed in the complex road conditions of curve turning and straight path obstacle avoidance, temperature management is insufficient, and multi-objective optimization capability is limited. And in the vehicle-level temperature of "-40 degrees to 125 degrees", the performance of MR fluid is best in the normal temperature (about 0 degrees to 40 degrees), so in order to ensure the system performance and the product reliability of the MR damper, temperature management is needed. SUMMARY

[0003] In order to solve the above problems in the prior art, the present application provides a MR semi-active suspension system control method based on environment sensing and prediction.

[0004] In order to achieve the above technical effects, the present application adopts the following scheme:

[0005] A MR semi-active suspension system control method based on environment sensing and prediction, comprising being provided with:

[0006] A detection module comprising an MR damper temperature estimation module for estimating the real-time temperature of the MR damper fluid, the MR damper temperature estimation module comprising a temperature sensor arranged on or near the MR damper;

[0007] A control module comprising an MR damper temperature control module for controlling the MR damper according to the real-time temperature of the MR damper, the MR damper temperature control module comprising a heating module arranged in the MR damper;

[0008] In the initial first calculation, the suspension is in a steady state condition, and the temperature is estimated by a heat balance equation. First, the output heat conducted to the environment by the MR damper shell is calculated based on the ambient temperature detected by the temperature sensor, and then the heat generated inside the MR damper is obtained by calculating the viscous dissipation heat of the MR fluid and the electromagnetic dissipation heat of the MR damper. The temperature of the MR fluid is estimated by balancing the generated heat and the output heat.

[0009] In subsequent motion, the suspension is in a dynamic condition, and the temperature change with time is estimated by numerical integration, so as to estimate the temperature of the MR fluid;

[0010] The temperature of the MR fluid is judged. If the temperature of the MR fluid is less than the normal temperature, the current delivered to the heating module is increased to warm up and increase the temperature of the MR fluid. If the temperature of the MR fluid is greater than the normal temperature, the current delivered to the heating module is reduced.

[0011] In a preferred embodiment, the detection module further includes a curve type estimation module, and the control module further includes a curve steering control module;

[0012] The curve type estimation module collects road information through the vehicle-mounted camera and estimates the maximum curvature R of the road within 10 meters ahead to determine whether the road type within 10 meters ahead of the car is a curve or a straight road.

[0013] The cornering control module controls the road based on the judgment result. If it is a curve, it implements MR damping control, which controls the current supplied to the coils of the four MR dampers to adjust the motion state of the four MR dampers. If it is a straight road, it implements MPC control.

[0014] In a preferred embodiment, the detection module further includes a road type detection module, which collects road surface image data through an on-board camera, performs semantic segmentation on the road surface image data, divides the road surface into five categories: asphalt road surface, cement road surface, gravel road surface, uneven road surface, and raised road surface, and assigns a separate weight parameter to each type of road surface.

[0015] In a preferred embodiment, the control module further includes a straight-line obstacle avoidance control module.

[0016] In a preferred embodiment, the heating module is disposed on the piston inside the MR damper and is in contact with the MR fluid.

[0017] Compared with existing technologies, the beneficial effects are:

[0018] It can control the temperature of the fluid inside the vehicle's four MR dampers to maintain normal operating temperatures (approximately 0 to 40 degrees Celsius) to optimize their performance and improve reliability. Attached Figure Description

[0019] Figure 1 This is a block diagram of the control method of the present invention.

[0020] Figure 2 It is an MR damping control strategy implemented for the four MR dampers of the whole vehicle.

[0021] Figure 3 These are five different weight parameters for five road categories. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0023] A control method for a semi-active MR suspension system based on environmental perception and prediction includes:

[0024] The detection module includes an MR damper temperature estimation module, used to estimate the real-time temperature T of the fluid in the MR damper. f The MR damper temperature estimation module includes a temperature sensor disposed on or near the MR damper;

[0025] The control module includes an MR damper temperature control module, which controls the MR damper according to the real-time temperature of the MR damper. The MR damper temperature control module includes a heating module installed inside the MR damper.

[0026] In the initial first calculation, the suspension is under steady-state conditions, and the estimation is performed using the thermal balance equation:

[0027] First, the ambient temperature T is detected by the temperature sensor. env Calculate the output heat P conducted from the MR damper housing to the environment. out ,

[0028] P out =h c ·A·(T f -T env )

[0029] Then, the heat P generated inside the MR damper is obtained by calculating the viscous heat loss of the MR fluid and the electromagnetic heat loss of the MR damper. in ,

[0030] P in =P viscous +P electromagnetic

[0031] Among them, P viscous It is the heat dissipation due to the viscosity of the MR fluid.

[0032] P viscous =η·γ·2·V

[0033] γ=v·h

[0034] Among them, P electromagnetic It is the electromagnetic dissipation and heat dissipation of the MR damper.

[0035] P electromagnetic =I·2·R

[0036] Therefore, according to the heat balance equation:

[0037] P in =P out

[0038] Right now,

[0039] η·γ·2·V+I·2·R=h c ·A·(T f -T env )

[0040] The temperature T of the MR fluid is obtained. f ,

[0041] T f =T env +(η·γ·2·V+I·2·R) / (h c ·A)

[0042] In the above, h c η is the thermal conductivity coefficient of the MR damper; A is the surface area of ​​the MR damper shell; η is the dynamic viscosity of the MR fluid; V is the volume of the MR fluid; R is the resistance of the coil inside the MR damper; I is the current through the coil inside the MR damper, which can be directly measured by setting a current detection module; h is the real-time height position of the MR damper, which can be directly measured by setting a position sensor on one side of the MR damper.

[0043] In subsequent motion, the suspension is under dynamic conditions, and the temperature change over time is estimated by numerical integration, thereby estimating the temperature of the MR fluid;

[0044] (C·dT f / dt)=P in -P out

[0045] Right now,

[0046] (C·dT f / dt)=η·γ·2·V+I·2·Rh c ·A·(T f -T env )

[0047] Where C is the heat capacity of the MR fluid; dT f / dt is the rate of change of temperature of the MR fluid over time.

[0048] The temperature of the MR fluid is determined. If the temperature of the MR fluid is lower than the ambient temperature, the current supplied to the heating module is increased to raise the temperature of the MR fluid. If the temperature of the MR fluid is higher than the ambient temperature, the current supplied to the heating module is reduced to a negligible level, or the current is reduced to a level that prevents the heating module from operating. In this way, the heat is carried away by the high-speed airflow in the vehicle movement to cool the fluid.

[0049] By using the methods described above, the temperature of the fluid inside the four MR dampers of the vehicle can be controlled at a normal temperature (approximately 0 to 40 degrees Celsius) to optimize their performance and improve reliability.

[0050] In a preferred embodiment, the detection module further includes a curve type estimation module, and the control module further includes a curve steering control module.

[0051] The curve type estimation module collects road information through the vehicle-mounted camera and estimates the maximum curvature R of the road within 10 meters ahead to determine whether the road type within 10 meters ahead of the car is a curve or a straight road.

[0052] When |R|>R C The road within 10 meters in front of the vehicle is a curve;

[0053] When |R|<=R C The road within 10 meters in front of the vehicle is a straight road;

[0054] In the formula, the unit of R is m. -1 ,R C Standard values ​​for road types, in meters (m). -1 And R C R is a threshold greater than 0; when the maximum curvature of the road within 10 meters in front of the vehicle is located on a left-turn road, R is positive; when the maximum curvature of the road within 10 meters in front of the vehicle is located on a right-turn road, R is negative.

[0055] The cornering control module determines the direction of travel based on the judgment result. If it is a curve, it implements MR damping control, controlling the current supplied to the coils of the four MR dampers to adjust their motion state. The control strategy is as follows: Figure 2 As shown; if it is a straight road, then MPC control is implemented.

[0056] in These are the dynamic deflections of the front left, front right, rear left, and rear left suspensions, which are obtained by differentiating the readings from the displacement sensors. and These are vehicle roll angle acceleration, roll angle velocity, vehicle pitch angle acceleration, and pitch velocity, respectively, which are obtained through the IMU (Inertial Measurement Unit). and It is a threshold.

[0057] The purpose is to suppress pitch and roll motions and improve vehicle handling during cornering.

[0058] In a preferred embodiment, the detection module further includes a road type detection module, which collects road surface image data through an on-board camera, performs semantic segmentation on the road surface image data, divides the road surface into five categories: asphalt road surface, cement road surface, gravel road surface, uneven road surface, and raised road surface, and assigns a separate weight parameter to each type of road surface.

[0059] The aforementioned MPC control algorithm is as follows:

[0060] When the road surface type acquisition module identifies one of the five types, the corresponding weight parameter is selected as the coefficient of the weighting matrix of the MPC cost function. q1,i " ρ1,i " q2,I " u ], and based on the unsprung displacement and velocity on the spring (via the accelerometer z'l

[0061] The four independent suspension systems are discretized using the forward Euler method, where x(k) represents the data collected by the sensors.

[0062] X(k+1)=Ax(k)+Bu(k)+Ew(k)

[0063] Y(k)=Cx(k)+Du(k)

[0064] In the formula: k is the current system time; x(k) is the current state variable; u(k) is the current control input variable; w(k) is the current external disturbance variable; y(k) is the control output variable; A, B, E, C, and D are system matrices of corresponding dimensions.

[0065] The predicted output of the system in the prediction time domain p is:

[0066] Y(k+1|k)=S x x(k)+S u u(k)+S d w(k)

[0067] In the formula,

[0068]

[0069] w(k)=[w(k) w(k+1) … w(k+m-1)] T .

[0070] Establish a cost function relating the predicted output variables of the suspension system and the control input variables of the actuators.

[0071] J(x(k),U(k))=||「 y (Y(k+1|k)-R p (k+1)|| 2 +||「u U(k)|| 2

[0072] In the formula, " y " u These are the weighting matrices for the predicted output and the control input, respectively; R p (k+1) is the reference sequence for controlling the output. y " u and R p The expression for (k+1) is:

[0073] " y =diag(" q1,i " ρ1,i " q2,I ), i = 1, 2, ..., p

[0074] " u =diag(" u，i ), i = 1, 2, ..., p

[0075] R p (k+1)=[r p (k+1)r p (k+2)···r p (k+p)] T

[0076] " q1,i " ρ1,i " q2,I , "u" represents the weighted weights for suspension dynamic deflection, tire dynamic deformation, sprung mass acceleration, and control input, respectively.

[0077] The actuator force u is solved by minimizing the cost function J(x(k),U(k)).

[0078] Constraints: Output range of actuator force

[0079] |F MR (I min )|<=|U OUT |<=|F MR (I max )|

[0080] Finally, the current I is obtained by using the inverse model of the MR damper.

[0081] In a preferred embodiment, the control module further includes a straight-line obstacle avoidance control module.

[0082] In a preferred embodiment, the heating module is disposed on the piston inside the MR damper and is in contact with the MR fluid.

[0083] In the description of this invention, it should be understood that the terms "upper," "lower," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0084] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0085] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

Claims

1. A control method for a semi-active MR suspension system based on environmental perception and prediction, characterized in that, Includes the following settings: The detection module includes an MR damper temperature estimation module for estimating the real-time temperature of the fluid in the MR damper. The MR damper temperature estimation module includes a temperature sensor disposed on or near the MR damper. The control module includes an MR damper temperature control module, which controls the MR damper according to the real-time temperature of the MR damper. The MR damper temperature control module includes a heating module installed inside the MR damper. In the initial first calculation, the suspension is under steady-state conditions. The heat is estimated by the thermal balance equation. First, the output heat of the MR damper shell is calculated to the environment by the ambient temperature detected by the temperature sensor. Then, the heat generated inside the MR damper is obtained by calculating the viscous heat loss of the MR fluid and the electromagnetic heat loss of the MR damper. The temperature of the MR fluid is estimated by balancing the generated heat with the output heat. In subsequent motion, the suspension is under dynamic conditions, and the temperature change over time is estimated by numerical integration, thereby estimating the temperature of the MR fluid; The temperature of the MR fluid is determined. If the temperature of the MR fluid is lower than the ambient temperature, the current supplied to the heating module is increased to raise the temperature of the MR fluid. If the temperature of the MR fluid is higher than the ambient temperature, the current supplied to the heating module is decreased.

2. The MR semi-active suspension system control method based on environmental perception and prediction as described in claim 1, characterized in that, The detection module also includes a curve type estimation module, and the control module also includes a curve steering control module; The curve type estimation module collects road information through the vehicle-mounted camera and estimates the maximum curvature R of the road within 10 meters ahead to determine whether the road type within 10 meters ahead of the car is a curve or a straight road. The cornering control module controls the road based on the judgment result. If it is a curve, it implements MR damping control, which controls the current supplied to the coils of the four MR dampers to adjust the motion state of the four MR dampers. If it is a straight road, it implements MPC control.

3. The MR semi-active suspension system control method based on environmental perception and prediction as described in claim 2, characterized in that, The detection module also includes a road type detection module, which collects road surface image data through an on-board camera, performs semantic segmentation on the road surface image data, and divides the road surface into five categories: asphalt road surface, cement road surface, gravel road surface, uneven road surface, and raised road surface, and assigns a separate weight parameter to each type of road surface.

4. The MR semi-active suspension system control method based on environmental perception and prediction as described in claim 2, characterized in that, The control module also includes a straight-line obstacle avoidance control module.

5. The MR semi-active suspension system control method based on environmental perception and prediction as described in claim 1, characterized in that, The heating module is mounted on the piston inside the MR damper and is in contact with the MR fluid.

Images