A hybrid vehicle suspension vibration reduction method based on magnetorheological damper

Abstract

The present invention discloses a hybrid vehicle suspension vibration reduction method based on a magnetorheological damper. The method first establishes a forward and inverse model of the magnetorheological damper based on test data, then uses a hybrid control algorithm to control the input current of the magnetorheological damper, and finally the magnetorheological damper outputs force to the vehicle suspension to complete the vibration reduction of the vehicle suspension. The present invention uses polynomial fitting methods of different accuracies to balance accuracy and simplicity, and establishes forward and inverse models of the magnetorheological damper. Based on the improved ceiling ON-OFF algorithm, the present invention introduces a floor ON-OFF algorithm to establish a new hybrid control algorithm that mainly uses the SMA indicator and takes into account the two indicators of SWS and DTD. The present invention can effectively control the vibration of the vehicle suspension, greatly improve the SMA, that is, the smoothness of the vehicle body, while also taking into account the two indicators of SWS and DTD.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical control, in particular to a hybrid vehicle suspension vibration reduction method based on a magnetorheological damper. Background Art

[0002] In the field of vehicle suspension control, researchers have consistently focused on how to effectively reduce suspension vibration and improve stability. Magnetorheological fluid, a smart material composed of fine particles mixed with a low-viscosity liquid, can be activated by high magnetic fields, forming a chain-like structure that alters its yield stress. Magnetorheological dampers based on magnetorheological fluids are increasingly being used in vehicle suspension control due to their safety, stability, fast response, strong shear resistance, and easy adjustment.

[0003] Vehicle suspensions are primarily classified into three categories: passive, active, and semi-active. Magnetorheological damper-based suspension control falls under the category of semi-active control. The inputs to the MR damper are the piston's displacement, velocity, and input current. The damper's internal parameters are closely related to the current, so adjusting the control current determines the damper's control. The control current of a MR damper is generally limited to between 0 and 2A, and in actual use is typically below 0.5A. This makes it more energy-efficient than active control. Furthermore, since the MR damper can still function as a passive control even when the current is 0A, it offers a "power-off, no-failure" feature compared to active control, making it more stable and secure.

[0004] Furthermore, classic vibration control algorithms based on vehicle state assessment include skyhook control, groundhook control, and acceleration damping control. However, skyhook control primarily targets the vehicle's vertical acceleration (SMA), ignoring suspension displacement (SWS) and wheel deflection (DTD). Groundhook control algorithms primarily target SWS or DTD, ignoring SMA. These three evaluation metrics, SMA, SWS, and DTD, are mutually exclusive and difficult to improve simultaneously. These classic algorithms fail to comprehensively address vehicle performance indicators. Summary of the Invention

[0005] The present invention aims to provide a hybrid vehicle suspension vibration reduction method based on magnetorheological dampers (MRDs). This method uses a hybrid control algorithm based on vehicle state determination to control the output of the MRDs, achieving semi-active vibration reduction control of the vehicle suspension. This method employs an improved vehicle state determination control algorithm to comprehensively consider the three indicators: SMA, SWS, and DTD. While prioritizing SMA, the control algorithm minimizes the impact of the SWS and DTD indicators.

[0006] The technical solution for achieving the objectives of the present invention is: a hybrid vehicle suspension vibration reduction method based on a magnetorheological damper, wherein the method controls the output of the magnetorheological damper on the vehicle suspension by a hybrid control method, and specifically comprises the following steps:

[0007] Step 1, establishing the forward and inverse models of the magnetorheological damper;

[0008] Step 2: Using a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper;

[0009] Step 3: The magnetorheological damper acts on the vehicle suspension to achieve vibration reduction of the vehicle suspension.

[0010] Furthermore, the forward and inverse models of the magnetorheological damper are established in step 1, and the specific process includes:

[0011] Step 1-1, performing a loading test on the magnetorheological damper using a fatigue testing machine to obtain experimental data under different currents;

[0012] Step 1-2, based on the data in step 1-1, perform parameter identification on the model of the magnetorheological damper by using a genetic algorithm;

[0013] Step 1-3, performing polynomial fitting on the current for the model parameters identified in step 1-2;

[0014] Step 1-4, performing a linear fit on the model parameters identified in step 1-2 with respect to the current;

[0015] Step 1-5: Based on step 1-3, a forward model of the magnetorheological damper is established;

[0016] Step 1-6: Based on step 1-4, the inverse model of the magnetorheological damper is obtained by reverse deduction.

[0017] Furthermore, the hybrid control algorithm in step 2 is specifically: on the basis of improving the ceiling ON-OFF algorithm, the floor shed ON-OFF algorithm is added. The hybrid control algorithm controls the output of the magnetorheological damper as follows:

[0018]

[0019] Among them, the damping coefficient c f The determination formula is as follows:

[0020]

[0021] Where F' is the optimal suspension control force, c f is the damping coefficient of the magnetorheological damper, c skymax is the maximum suspension ceiling damping, c skyminis the minimum suspension ceiling damping, c groundmax is the maximum suspension floor damping, c groundmin is the minimum suspension floor damping, is the velocity of the unsprung mass of the suspension, is the velocity of the sprung mass of the suspension, and α and β are both constants not less than zero.

[0022] A hybrid vehicle suspension vibration reduction system based on a magnetorheological damper, the system comprising:

[0023] The first module is used to establish the forward and inverse models of magnetorheological dampers;

[0024] The second module uses a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper.

[0025] The third module is used to apply the magnetorheological damper to the vehicle suspension to achieve vibration reduction of the vehicle suspension.

[0026] Compared with the prior art, the present invention has the following significant advantages:

[0027] 1) The present invention establishes the forward and inverse models of the magnetorheological damper by using polynomial fitting methods with different precisions, taking into account both accuracy and simplicity.

[0028] 2) Based on the improved ceiling ON-OFF algorithm, the present invention introduces the floor shed ON-OFF algorithm and establishes a new hybrid control algorithm that mainly uses the SMA indicator and takes into account the two indicators of SWS and DTD.

[0029] The present invention is further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 1 is a principle block diagram of a hybrid vehicle suspension vibration reduction method based on a magnetorheological damper in one embodiment.

[0031] Figure 2 : Parameter identification results of a magnetorheological damper based on a hyperbolic tangent model at different currents (0A, 0.5A, 1A, 1.5A, 2A, 2.5A) identified based on a GA algorithm in one embodiment, wherein Figure 2 (a) is a comparison diagram of the force-velocity fitting curve and the test curve. Figure 2 (b) is a comparison diagram of the force-displacement fitting curve and the test curve.

[0032] Figure 3 A schematic diagram of a process for verifying the accuracy of a magnetorheological damper inverse model in one embodiment.

[0033] Figure 4The figure is a comparison diagram of the current output by the inverse model and the expected control current under the conditions of randomly generated current, displacement, and velocity input of the magnetorheological damper model established in one embodiment.

[0034] Figure 5 This is a comparison diagram of the actual output and expected output of the forward model under the control of the inverse model output current of the magnetorheological damper model established in one embodiment under the conditions of randomly generated current, displacement, and velocity input.

[0035] Figure 6 Schematic diagram of the control current of a magnetorheological damper under the control of a hybrid control algorithm under the excitation of a Class B road in one embodiment.

[0036] Figure 7 1 is a comparison diagram of the actual output of the magnetorheological damper under the control of the hybrid control algorithm and the expected output calculated by the hybrid control algorithm under the excitation of a Class B road in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0038] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0039] In one embodiment, combined Figure 1 , provides a hybrid vehicle suspension vibration reduction method based on magnetorheological damper, including the following contents:

[0040] (1) Parameter identification of magnetorheological damper: The magnetorheological damper is subjected to a loading test using a fatigue testing machine to obtain experimental data under different currents;

[0041] (2) Establishment of the forward model of the MR damper: The identified model parameters are fitted with a high-precision polynomial with respect to the current to obtain the forward model of the MR damper, i.e., F = f(i, s, v), where F is the output of the MR damper, i is the control current of the MR damper, s is the piston displacement of the MR damper, and v is the piston displacement velocity of the MR damper.

[0042] (3) Establishment of the inverse model of the MR damper: The identified model parameters are linearly fitted with respect to the current, and the inverse model of the MR damper is directly obtained by the inverse method, i.e., i = f(F, s, v), where i is the control current of the MR damper, F is the desired output of the MR damper, s is the piston displacement of the MR damper, and v is the piston displacement velocity of the MR damper;

[0043] (4) Hybrid control algorithm to determine the optimal suspension control force: The ground shed ON-OFF control algorithm is introduced into the improved sky shed ON-OFF algorithm. The resulting hybrid control algorithm is used to determine the damping coefficient of the magnetorheological damper. The algorithm is as follows:

[0044] The hybrid control algorithm determines the damping coefficient of the magnetorheological damper as follows:

[0045]

[0046] The optimal suspension control force is

[0047]

[0048] Where F' is the optimal suspension control force, c f is the damping coefficient of the magnetorheological damper, c skymax is the maximum suspension ceiling damping, c skymin is the minimum suspension ceiling damping, c groundmax is the maximum suspension floor damping, c groundmin is the minimum suspension floor damping, is the velocity of the unsprung mass of the suspension, is the velocity of the sprung mass of the suspension, and α and β are both constants not less than zero.

[0049] (5) Establishment of the dynamic equation of the semi-active suspension: The magnetorheological damper model is used to establish the semi-active suspension structure of a 1 / 4 vehicle with 2 degrees of freedom. The dynamic equation of the semi-active suspension is obtained based on the analysis of Newton's law of dynamics.

[0050]

[0051] Where m s is the sprung mass, m t is the unsprung mass, x sis the vertical displacement of the sprung mass, x t is the vertical displacement of the unsprung mass, x r For road motivation, is the vertical velocity of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the sprung mass, k s is the suspension stiffness, k t is the tire stiffness, c f is the damping coefficient of the magnetorheological damper.

[0052] In one embodiment, a hybrid vehicle suspension vibration reduction system based on a magnetorheological damper is provided, the system comprising:

[0053] The first module is used to establish the forward and inverse models of magnetorheological dampers;

[0054] The second module uses a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper.

[0055] The third module is used to apply the magnetorheological damper to the vehicle suspension to achieve vibration reduction of the vehicle suspension.

[0056] The specific definitions of the hybrid vehicle suspension and vibration reduction system based on magnetorheological dampers can be found in the above-mentioned definitions of the hybrid vehicle suspension and vibration reduction system based on magnetorheological dampers and will not be further elaborated here. Each module in the aforementioned hybrid vehicle suspension and vibration reduction system based on magnetorheological dampers can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0057] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0058] Step 1, establishing the forward and inverse models of the magnetorheological damper;

[0059] Step 2: Using a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper;

[0060] Step 3: The magnetorheological damper acts on the vehicle suspension to achieve vibration reduction of the vehicle suspension.

[0061] The specific definition of each step can be found in the above definition of the hybrid vehicle suspension vibration reduction method based on magnetorheological damper, which will not be repeated here.

[0062] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0063] Step 1, establishing the forward and inverse models of the magnetorheological damper;

[0064] Step 2: Using a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper;

[0065] Step 3: The magnetorheological damper acts on the vehicle suspension to achieve vibration reduction of the vehicle suspension.

[0066] The specific definition of each step can be found in the above definition of the hybrid vehicle suspension vibration reduction method based on magnetorheological damper, which will not be repeated here.

[0067] In one embodiment, the present invention is further verified and described in detail. The present invention proposes a hybrid vehicle suspension vibration reduction method based on a magnetorheological damper, comprising: first, establishing a forward and inverse model of the magnetorheological damper based on test data; then, using a hybrid control algorithm to control the input current of the magnetorheological damper; and finally, having the magnetorheological damper output force to the vehicle suspension to achieve vibration reduction of the vehicle suspension.

[0068] Step S1: Parameter identification of magnetorheological damper based on hyperbolic tangent model:

[0069] The hyperbolic tangent model of the magnetorheological damper is:

[0070]

[0071] Where, F MRD is the output of the magnetorheological damper, is the speed of the damper piston, x is the displacement of the damper piston, c is the post-yield viscosity coefficient, k is the stiffness coefficient, α is the hysteresis scaling factor, z is the hysteresis variable given by the hyperbolic tangent function, f0 is the damping force offset, β is the hysteresis slope scaling factor, and δ is the hysteresis width scaling factor.

[0072] Based on the fatigue testing machine loading test data of the MR damper, the GA algorithm was used to identify the six parameters c, k, α, f0, β, and δ in the model. The loading test data included the test time, MR damper piston displacement, MR damper piston movement speed, and MR damper output under the conditions of MR damper loading currents of 0A, 0.2A, 0.4A, 0.6A, 0.8A, and 1A, respectively.

[0073] Step S2: Establish the forward model of the magnetorheological damper:

[0074] The identified model parameters are fitted with high-precision polynomials with respect to the current. For example, the high-precision polynomial fitting results of the six parameters with respect to the current are as follows:

[0075] c=-2.605i 3 +4.099i 2 +3.984i+0.5754

[0076] k=-3.32i 4 +6.686i 3 -3.741i 2 +0.1141i+0.1148

[0077] α=-416i 3 +275.4i 2 +889.6i+35.35

[0078]

[0079] β=0.1545i 2 -0.2885i+0.3099

[0080] δ=1.501i+0.8425

[0081] Where c is the post-yield viscosity coefficient, k is the stiffness coefficient, and α is the hysteresis proportional factor. is the mean value of the damping force offset parameter f0, β is the hysteresis slope proportional factor, δ is the hysteresis width proportional factor, and i is the loading current of the magnetorheological damper.

[0082] Step S3: Establish an inverse model of the magnetorheological damper:

[0083] The identified model parameters are linearly fitted with respect to the current, and the inverse model of the magnetorheological damper is directly obtained by the inverse method. For example, the linear polynomial fitting results of the six parameters with respect to the current are as follows:

[0084] c=5.603i+0.4875

[0085] k=-0.2637i+0.07086

[0086] α=769i+71.86

[0087]

[0088]

[0089]

[0090] By inverse deduction, the obtained inverse model can be written as:

[0091]

[0092] Where F is the output of the magnetorheological damper, is the displacement velocity of the piston of the magnetorheological damper, and x is the displacement of the piston of the magnetorheological damper.

[0093] Step S4: The hybrid control algorithm determines the optimal suspension control force:

[0094] The ground shed ON-OFF control algorithm is introduced into the improved ceiling ON-OFF algorithm to obtain a hybrid control algorithm to determine the damping coefficient of the magnetorheological damper. The algorithm is as follows:

[0095] The hybrid control algorithm determines the damping coefficient of the magnetorheological damper as follows:

[0096]

[0097] The optimal suspension control force is:

[0098]

[0099] Where c f is the damping coefficient of the magnetorheological damper, c skymax is the suspension ceiling damping, c groundmin is the suspension floor damping, is the velocity of the unsprung mass of the suspension, is the velocity of the sprung mass of the suspension, α and β are constants not less than zero, and F' is the optimal suspension control force.

[0100] For the improved ceiling ON-OFF algorithm, considering its performance comprehensively, β is taken as 0.8 in the simulation.

[0101] For hybrid control, the SMA indicator is still the focus. Therefore, the improved ceiling ON-OFF algorithm accounts for 87% of the hybrid control, that is, α is taken as 0.87, and the ground shed ON-OFF algorithm accounts for 13%. In order to improve the SWS and DTD indicators, the deterioration of the SMA indicator is not significant.

[0102] like Figure 2 As shown in the figure, the parameter identification results of the magnetorheological damper based on the hyperbolic tangent model under different currents identified by the GA algorithm are very accurate in fitting the experimental data and the model identification results, which lays the foundation for the subsequent establishment of the inverse model.

[0103] like Figure 3As shown in the figure, in order to verify the accuracy of the MR damper inverse model, the three parameters of the randomly generated current (i_desired), piston displacement, and displacement velocity are transmitted to the established high-precision forward model to obtain the desired MR damper output (F_desired). This data is transmitted to the established MR damper inverse model, and the accuracy of the inverse model is verified by comparing the current (i_MRD) output by the inverse model with the randomly generated current (i_desired). Finally, the current (i_MRD) output by the inverse model is transmitted to the forward model to obtain the actual MR damper output (F_MRD), which is compared with the desired MR damper output (F_desired) to verify the accuracy of the established MR damper model.

[0104] like Figure 4 As shown in the figure, the output current of the established magnetorheological damper inverse model tracks the expected control current well, which proves that the inverse model of the magnetorheological damper obtained by directly inverse deduction using a first-order polynomial fitting is more accurate.

[0105] like Figure 5 As shown in the figure, the actual output of the established magnetorheological damper forward model under the control of the inverse model output current tracks the expected output well, which further verifies the accuracy of the established magnetorheological damper.

[0106] like Figure 6 As shown in Figure 3, the control current of the magnetorheological damper under the control of the hybrid control algorithm under the excitation of Class B road surface.

[0107] like Figure 7 As shown in Figure 3, under the excitation of Class B road surface, the actual output of the magnetorheological damper under the control of the hybrid control algorithm is compared with the expected output calculated by the hybrid control algorithm.

[0108] Under the excitation of Class B road surface, the vibration response indicators of passive control, improved skylight ON-OFF control, groundlight ON-OFF control and hybrid control are shown in Table 1 below.

[0109] Table 1 Vibration response index

[0110]

[0111] As can be seen from the table above, the improved hybrid predictive algorithm still improves SMA by 15.46%. Meanwhile, compared to the passive control algorithm, the SWS index deteriorates by only 6.25%, and compared to the improved skyhook onoff control algorithm, it improves by 7.27%. Compared to the passive control algorithm, the DTD index deteriorates by only 15%, and compared to the improved skyhook control algorithm, it improves by 8%. This shows that the hybrid control algorithm improved by the groundhook onoff algorithm can effectively control vehicle suspension vibration, significantly improving SMA (i.e., vehicle body smoothness) while also taking into account both SWS and DTD.

[0112] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only illustrative of the principles of the present invention. Without departing from the spirit and scope of the present invention, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A hybrid vehicle suspension vibration reduction method based on magnetorheological damper, characterized in that: The method controls the output of a magnetorheological damper on a vehicle suspension using a hybrid control method, specifically comprising the following steps: Step 1, establishing the forward and inverse models of the magnetorheological damper; Step 2: Using a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper; Step 3: The magnetorheological damper acts on the vehicle suspension to achieve vibration reduction of the vehicle suspension; Step 1 describes the establishment of the forward and inverse models of the magnetorheological damper. The specific process includes: Step 1-1, performing a loading test on the magnetorheological damper using a fatigue testing machine to obtain experimental data under different currents; Step 1-2, performing parameter identification on the magnetorheological damper model based on the data in step 1-1; Step 1-3, performing polynomial fitting on the current for the model parameters identified in step 1-2; Step 1-4, performing a linear fit on the model parameters identified in step 1-2 with respect to the current; Step 1-5: Based on step 1-3, a forward model of the magnetorheological damper is established; Step 1-6: Based on step 1-4, reverse deduction is performed to obtain the inverse model of the magnetorheological damper; The hybrid control algorithm described in step 2 is specifically: on the basis of improving the ceiling ON-OFF algorithm, the floor shed ON-OFF algorithm is added. The hybrid control algorithm controls the output of the magnetorheological damper as follows: Among them, the damping coefficient c f The determination formula is as follows: Where F' is the optimal suspension control force, c f is the damping coefficient of the magnetorheological damper, c skymax is the maximum suspension ceiling damping, c skymin is the minimum suspension ceiling damping, c groundmax is the maximum suspension floor damping, c groundmin is the minimum suspension floor damping, is the velocity of the unsprung mass of the suspension, is the velocity of the sprung mass of the suspension, and α and β are both constants not less than zero.

2. The hybrid vehicle suspension vibration reduction method based on magnetorheological damper according to claim 1, characterized in that: In step 1-2, the parameters of the magnetorheological damper model are identified specifically through a genetic algorithm.

3. The hybrid vehicle suspension vibration reduction method based on magnetorheological damper according to claim 2, characterized in that: The forward model of the magnetorheological damper in steps 1-5 is: F = f(i, s, v), where F is the output of the magnetorheological damper, i is the control current of the magnetorheological damper, s is the piston displacement of the magnetorheological damper, and v is the piston displacement speed of the magnetorheological damper.

4. The hybrid vehicle suspension vibration reduction method based on magnetorheological damper according to claim 3, characterized in that: The inverse model of the magnetorheological damper in steps 1-6 is: i=f(F,s,v).

5. A hybrid vehicle suspension vibration reduction system based on a magnetorheological damper according to the method of any one of claims 1 to 4, characterized in that: The system comprises: The first module is used to establish the forward and inverse models of magnetorheological dampers; The second module uses a hybrid control algorithm based on vehicle state determination to control the output of the magnetorheological damper. The third module is used to apply the magnetorheological damper to the vehicle suspension to achieve vibration reduction of the vehicle suspension.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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