Method, device, equipment, medium and product for controlling a hydraulic suspension

Abstract

Embodiments of the present application provide a control method, device, equipment, medium and product of a hydraulic mount. The method comprises: obtaining a parameter group affecting the stiffness of the hydraulic mount; inputting the parameter group affecting the stiffness of the hydraulic mount into a first curve model to obtain a first frequency response curve; performing multi-objective optimization on the parameter group according to the first frequency response curve and a second frequency response curve to obtain a multi-objective optimization result; the second frequency response curve is a curve output by a second curve model; the first curve model and the second curve model are both models obtained by performing multi-scale analysis on the parameter group; and adjusting a response parameter of the hydraulic mount according to the multi-objective optimization result. The method is used to improve the use effect of stiffness control adjustment.

Description

Technical Field

[0001] This application relates to the field of automotive powertrain technology, and in particular to a control method, device, equipment, medium, and product for hydraulic suspension. Background Technology

[0002] In the field of automotive powertrain vibration control, hydraulic mounts, as core vibration isolation components, achieve multi-frequency vibration isolation capabilities based on traditional rubber mounts through the dynamic coupling of the fluid chamber structure and the throttling channel. However, their fixed stiffness and damping characteristics are difficult to meet the requirements of complex operating conditions, which has driven the development of active mount technology. Active components based on electromagnetic, piezoelectric, or electro-hydraulic actuators dynamically optimize system response by adjusting control force in real time. They have been applied in high-end models, but their high cost and energy consumption have limited their widespread adoption. In recent years, quasi-zero stiffness (QZS) mount technology, through the design of a nonlinear elastic mechanism, forms an extremely low stiffness region near static equilibrium, breaking through the frequency response limitations of traditional linear systems and providing a new path for low-frequency, high-efficiency vibration isolation.

[0003] Currently, in suspension systems, existing methods input sinusoidal excitation signals with increasing frequency and measure the system's force-displacement and force transmissibility characteristics to provide data support for parameter identification. For example, in air suspension design based on quasi-zero stiffness (QZS) theory, frequency sweep experiments can determine the synergistic parameters of the disc spring assembly (negative stiffness) and the air spring (positive stiffness), enabling the system to achieve quasi-zero stiffness characteristics near static equilibrium. Stiffness-displacement expressions are established through static analysis, and combined with a two-degree-of-freedom dynamic model, structural parameters are optimized to improve low-frequency vibration isolation performance.

[0004] However, existing methods still suffer from poor performance when dealing with complex data. Summary of the Invention

[0005] This application provides a control method, device, equipment, medium, and product for hydraulic suspension, which aims to improve the stiffness control and adjustment effect.

[0006] In a first aspect, embodiments of this application provide a control method for a hydraulic suspension, including:

[0007] Obtain the parameter set that affects the stiffness of the hydraulic suspension;

[0008] The parameter set affecting the stiffness of the hydraulic suspension is input into the first curve model to obtain the first frequency response curve;

[0009] Based on the first and second frequency response curves, multi-objective optimization is performed on the parameter set to obtain the multi-objective optimization result. The second frequency response curve is the curve output by the second curve model. Both the first and second curve models are models obtained by multi-scale analysis of the parameter set. Specifically, when the first frequency response curve output by the first curve model is the stiffness frequency response curve, the second frequency response curve output by the second curve model is the phase frequency response curve, and when the first frequency response curve output by the first curve model is the phase frequency response curve, the second frequency response curve output by the second curve model is the stiffness frequency response curve.

[0010] Based on the results of multi-objective optimization, the response parameters of the hydraulic suspension are adjusted.

[0011] In one possible implementation, the parameter set includes the equivalent mass of the hydraulic components in the hydraulic mount, fluid damping, phase angle, and external compression stroke.

[0012] The parameter set affecting the stiffness of the hydraulic suspension is input into the first curve model to obtain the first frequency response curve, including:

[0013] Based on the equivalent mass, fluid damping, phase angle, and external compression stroke of the hydraulic components in the hydraulic mount, determine the response parameters of the hydraulic components in the hydraulic mount. The response parameters shall include at least one of the following parameters: phase difference, amplitude, offset, nonlinear stiffness coefficient, damping parameter, and excitation force amplitude.

[0014] The response parameters of the hydraulic components in the hydraulic suspension are input into the first curve model to obtain the first frequency response curve output by the first curve model.

[0015] In one possible implementation, when the first frequency response curve is a stiffness frequency response curve, the first curve model satisfies:

[0016] ;

[0017] in, For amplitude, For nonlinear stiffness coefficients, The damping coefficient is... This is the offset. , and It is a dimensionless number. The magnitude of the external force.

[0018] In one possible implementation, when the first frequency response curve is a phase frequency response curve, the first curve model satisfies:

[0019] ;

[0020] in, The phase angle, For amplitude, This is the offset. For nonlinear stiffness coefficients, The damping coefficient;

[0021] , and It is a dimensionless number. The magnitude of the external force.

[0022] In one possible implementation, the method further includes:

[0023] Determine the equations of motion for the hydraulic components in a hydraulic suspension system;

[0024] By introducing multiple time scales into the motion equations of hydraulic components in a hydraulic suspension, nonlinear ordinary differential equations are obtained.

[0025] The constraint equations are obtained by solving the nonlinear ordinary differential equations.

[0026] Substituting the parameter set affecting the stiffness of the hydraulic suspension into the constraint equation, the first curve model is obtained.

[0027] In one possible implementation, multi-objective optimization is performed on the parameter set based on the first frequency response curve and the second frequency response curve to obtain the multi-objective optimization result, including:

[0028] Based on the differences between corresponding points on the first and second frequency response curves, an optimization function for multi-objective optimization is constructed.

[0029] Based on the optimization function used for multi-objective optimization, multi-objective optimization is performed on the parameter set to obtain the multi-objective optimization result.

[0030] In one possible implementation, an optimization function for multi-objective optimization is constructed based on the differences between corresponding points on the first and second frequency response curves, including:

[0031] Based on the differences between corresponding points on the first and second frequency response curves, a mean square error curve is generated.

[0032] Based on the mean squared error curve, a cost function is generated to represent the optimization function used for multi-objective optimization.

[0033] In one possible implementation, obtaining a set of parameters affecting the stiffness of the hydraulic suspension includes:

[0034] The excitation signal in the road surface excitation is obtained, which is obtained by the sensor detecting the road surface or by the simulation results of the road surface in the simulation system;

[0035] The design optimization objective is determined based on the excitation signal in the road surface excitation.

[0036] Based on the design optimization objectives, obtain the parameter set that affects the stiffness of the hydraulic suspension.

[0037] Secondly, embodiments of this application provide a control device for a hydraulic suspension, comprising:

[0038] The acquisition module is used to acquire a set of parameters that affect the stiffness of the hydraulic suspension.

[0039] The first module is used to input the parameter set that affects the stiffness of the hydraulic suspension into the first curve model to obtain the first frequency response curve;

[0040] The second obtaining module is used to perform multi-objective optimization on the parameter set based on the first frequency response curve and the second frequency response curve to obtain the multi-objective optimization result; the second frequency response curve is the curve output by the second curve model, and both the first curve model and the second curve model are models obtained by multi-scale analysis of the parameter set; wherein, when the first frequency response curve output by the first curve model is the stiffness frequency response curve, the second frequency response curve output by the second curve model is the phase frequency response curve, and when the first frequency response curve output by the first curve model is the phase frequency response curve, the second frequency response curve output by the second curve model is the stiffness frequency response curve;

[0041] The adjustment module is used to adjust the response parameters of the hydraulic suspension based on the results of multi-objective optimization.

[0042] Thirdly, embodiments of this application provide a hydraulic suspension device, including: a memory and a processor;

[0043] The memory stores instructions that the computer executes;

[0044] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0046] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0047] The hydraulic suspension control method, device, equipment, and product provided in this application determine the parameter set affecting the stiffness of the hydraulic suspension, as well as the corresponding stiffness frequency response curve and phase frequency response curve. The stiffness frequency response curve characterizes the relationship between the stiffness of the hydraulic suspension and frequency, and the phase frequency response curve characterizes the relationship between the phase relationship between the input excitation and the output response of the hydraulic suspension and frequency. Based on the stiffness frequency response curve and phase frequency response curve corresponding to the parameter set, multi-objective optimization is performed on the parameters in the parameter set to obtain the multi-objective optimization result. Based on the multi-objective optimization result, the means of controlling the hydraulic suspension are determined by identifying the parameter set affecting the stiffness of the hydraulic suspension and obtaining the relationship between its stiffness and frequency, as well as the relationship between the phase relationship between the input excitation and the output response and frequency. Based on this, multi-objective optimization is performed on the parameter set to find a solution that optimally balances stiffness and phase characteristics under different working conditions. The real-time control strategy of the hydraulic suspension is adjusted according to the obtained multi-objective optimization result, thereby effectively improving its vibration isolation performance, reducing unnecessary vibration transmission, and improving ride comfort and mechanical system stability. This ultimately achieves the desired effect of improved stiffness control and adjustment. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] Figure 1 A schematic diagram of a scenario for the control method of the hydraulic suspension provided in this application;

[0050] Figure 2 Flowchart of the control method for the hydraulic suspension provided in this application Figure 1 ;

[0051] Figure 3 Flowchart of the control method for the hydraulic suspension provided in this application Figure 2 ;

[0052] Figure 3a A schematic diagram illustrating the cost objective function descent effect in parameter identification provided in the embodiments of this application;

[0053] Figure 4 A schematic diagram of the control device for the hydraulic suspension provided in this application;

[0054] Figure 5 A schematic diagram of the hydraulic suspension device provided in this application.

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0057] First, let me explain the terms used in this application:

[0058] The stiffness frequency response curve refers to the relationship between the equivalent stiffness of a hydraulic suspension and frequency under different excitation frequencies. This curve reflects the hydraulic suspension's resistance to vibration over a wide frequency range.

[0059] The phase frequency response curve refers to the curve showing the phase difference between the input force and the output displacement (or acceleration) of a hydraulic mount under different frequency excitations as a function of frequency. It can describe the dynamic hysteresis characteristics of the hydraulic mount and is used to determine the stability and delay of the system response.

[0060] The stiffness of a hydraulic mount can refer to the restoring force required per unit displacement exhibited by the combined action of its internal structure (including rubber, liquid cavity, and flow restriction channel, etc.) when subjected to external excitation; that is, the equivalent stiffness of the system.

[0061] In hydraulic suspension, hydraulic components can refer to the core components inside the hydraulic suspension used to transmit or regulate fluid pressure and achieve dynamic damping and stiffness control.

[0062] The equivalent mass of a hydraulic component can refer to the concentrated mass value of the fluid and mechanical parts involved in the movement of the hydraulic component during vibration, converted to the direction of the principal vibration.

[0063] The fluid damping of hydraulic components refers to the energy dissipation effect generated by the flow of fluid in the flow-limiting orifice or channel in a hydraulic suspension, which manifests as the ability to suppress vibration.

[0064] The phase angle of a hydraulic component refers to the time lag angle of the output response (such as displacement or force) of the hydraulic component relative to the input excitation signal under periodic excitation, reflecting the comprehensive performance of the system's internal energy storage and dissipation characteristics.

[0065] The external compression stroke of a hydraulic component can refer to the maximum compressive deformation that the main load-bearing structure of a hydraulic suspension can withstand when subjected to external loads or excitation.

[0066] In existing technologies, hydraulic mounts, as an important connection device between the automotive powertrain and the vehicle frame, are widely used in modern passenger cars and commercial vehicles. Compared with traditional rubber mounts, hydraulic mounts, due to their excellent frequency response characteristics and multi-modal vibration reduction capabilities, can effectively isolate vibrations generated by engine operation, thereby improving ride comfort and NVH performance.

[0067] Traditional hydraulic suspensions typically consist of a rubber elastomer, a fluid chamber structure, and a throttling channel. By regulating the flow of fluid between different chambers, the system's damping characteristics are adjusted. However, their stiffness and damping are often fixed, making it difficult to adapt to dynamic requirements under various operating conditions and frequency bands.

[0068] However, with the development of control theory and actuator technology, active suspension systems have gradually become a research hotspot. Active elements (such as electromagnetic actuators, piezoelectric actuators, or electro-hydraulic actuators) can actively apply control forces according to the vehicle's state, thereby adjusting the dynamic response of the suspension system in real time.

[0069] Currently, some high-end models have begun to use electromagnetic active suspension components to improve the overall vehicle dynamic performance. Although active components have excellent adjustment capabilities, their cost and energy consumption limit their adoption in mid-to-low-end models.

[0070] Quasi-Zero Stiffness (QZS) suspension is a low-stiffness vibration isolation system based on a nonlinear elastic mechanism. Its core idea is to create an extremely low-stiffness region near the static equilibrium of the system through mechanical structural design or multi-element coupling, thereby achieving low-frequency, high-performance vibration isolation.

[0071] However, traditional QZS structures mostly employ mechanical parallel structures, such as spring-rod-hinge systems, which are large in size and have a limited adjustment range. Some studies have attempted to introduce magnetorheological fluids, smart materials, or piezoelectric elements into QZS design to achieve dynamic adjustment of stiffness, but these still face problems such as slow response speed and large parameter fluctuations in practical applications.

[0072] The hydraulic suspension control method provided in this application, through a pre-set first curve model (for generating stiffness frequency response curves) and a second curve model (for generating phase frequency response curves), can quickly generate the dynamic response characteristic curves of the system after obtaining parameter set data under the current operating conditions, without relying on traditional frequency sweep experiments or complex numerical identification processes. Based on these stiffness and phase frequency response curves directly output by the models, and further combined with a multi-objective optimization algorithm, it can efficiently weigh multiple performance objectives and quickly solve for the optimal parameter combination adapted to the current system state. Since the entire process highly relies on the parameter adaptive update mechanism driven by the analytical model and optimization algorithm, rather than fixed parameter settings, even when system parameters fluctuate (such as fluid damping changing with temperature or stiffness shifting with aging), the control strategy can be dynamically corrected by adjusting the input parameters in real time or near real time and recalculating the response curves, thereby suppressing performance deviations caused by parameter fluctuations. This significantly improves the robustness and stability of the hydraulic suspension system under complex and variable operating conditions, effectively alleviating the control failure or performance degradation problems caused by parameter uncertainty in traditional control methods.

[0073] Figure 1 A schematic diagram of a scenario for the control method of the hydraulic suspension provided in this application, such as... Figure 1 As shown, the specific application scenario of this application is a hydraulic suspension in a vehicle. The hydraulic suspension is equipped with a processor, which can be a programmable hardware chip (Field-Programmable Gate Array, FPGA). After obtaining the parameter set affecting the stiffness of the hydraulic suspension, it can generate stiffness frequency response curves and phase frequency response curves according to the built-in first curve model and second curve model. The stiffness frequency response curves and phase frequency response curves are then optimized in multiple objectives. After obtaining the multi-objective optimization results, the highly responsive electromagnetic active component in the hydraulic suspension directly acts on the structure inside the fluid cavity to achieve dynamic control of the suspension stiffness.

[0074] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0075] Figure 2 Flowchart of the control method for the hydraulic suspension provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0076] S201. Obtain the parameter set that affects the stiffness of the hydraulic suspension.

[0077] The parameter set affecting the stiffness of the hydraulic mount refers to the set of physical parameters used to determine the dynamic stiffness characteristics of the hydraulic mount. The parameter types and ranges in this set can be preset based on the user's expert experience or selected according to actual needs. After determining the parameter types and ranges, different parameter sets can be randomly generated.

[0078] In the embodiments of this application, the parameter set affecting the stiffness of the hydraulic suspension can be characterized as an individual in a population performing multi-objective optimization. This population can be determined according to a pre-set parameter range, and each individual in the population carries parameters.

[0079] In this embodiment of the application, obtaining the parameter set affecting the stiffness of the hydraulic suspension includes:

[0080] The excitation signal in the road surface excitation is obtained, which is obtained by the sensor detecting the road surface or by the simulation results of the road surface in the simulation system;

[0081] The design optimization objective is determined based on the excitation signal in the road surface excitation.

[0082] Based on the design optimization objectives, obtain the parameter set that affects the stiffness of the hydraulic suspension.

[0083] Among them, the excitation signal in road excitation can refer to the vibration signal extracted from the external road excitation received by the vehicle during driving. This signal can characterize the dynamic input caused by different road conditions to the vehicle chassis and suspension system.

[0084] The excitation signal in road surface excitation can be obtained from the detection of the road surface by sensors or from the simulation results of the road surface in the simulation system. Specifically:

[0085] The excitation signal is obtained by the sensor detecting the road surface. This can refer to the real-time acquisition of vibration data caused by uneven road surface during actual vehicle operation by installing devices such as acceleration sensors, displacement sensors, or force sensors on the vehicle, and then processing this data as an external excitation signal input.

[0086] Based on the simulation results of the road surface in the simulation system, it can be seen that in a virtual simulation environment, a digital road surface model can be constructed based on the standard road surface spectrum or measured road data. Combined with multibody dynamics simulation or finite element analysis methods, the vibration excitation experienced by the vehicle under specific driving conditions can be simulated, thereby generating excitation signals for system analysis.

[0087] Design optimization objectives can refer to setting optimization directions and technical indicators for the hydraulic suspension system under different operating conditions based on the characteristics of excitation signals obtained from acquisition or simulation, combined with the performance requirements of the vehicle. These objectives include, but are not limited to, achieving low dynamic stiffness in the low-frequency band, high damping response in the wide-frequency band, reducing phase lag, or improving ride comfort. In the embodiments of this application, the design optimization objective can be the setting objective of setting the stiffness of the hydraulic suspension to zero.

[0088] Based on the design optimization objectives, obtaining the parameter set affecting the stiffness of the hydraulic mount can refer to identifying and selecting key parameters that have a significant impact on the design optimization objectives, based on the structural principles and dynamic model of the hydraulic mount, under the premise of clearly defined optimization objectives. For example, when the design optimization objective is stiffness characteristics, the parameter set, based on expert experience, may include the equivalent mass of the hydraulic components in the hydraulic mount, fluid damping, phase angle, and external compression stroke.

[0089] Based on this, by real-time acquisition or simulation of road excitation signals experienced by the vehicle during driving, and by setting clear design optimization goals (such as achieving near-zero stiffness and wide-frequency high-damping response) in conjunction with the overall vehicle performance requirements, and by identifying key parameter sets affecting stiffness characteristics based on the structural and dynamic model of the suspension system, we can provide theoretical basis and technical support for the dynamic characteristic analysis and optimization control of hydraulic suspension, thereby achieving the effects of improving vibration isolation performance and enhancing system adaptability and stability.

[0090] S202. Input the parameter set that affects the stiffness of the hydraulic suspension into the first curve model to obtain the first frequency response curve;

[0091] S203. Based on the first frequency response curve and the second frequency response curve, perform multi-objective optimization on the parameter set to obtain the multi-objective optimization result; the second frequency response curve is the curve output by the second curve model, and both the first curve model and the second curve model are models obtained by multi-scale analysis of the parameter set; wherein, when the first frequency response curve output by the first curve model is the stiffness frequency response curve, the second frequency response curve output by the second curve model is the phase frequency response curve, and when the first frequency response curve output by the first curve model is the phase frequency response curve, the second frequency response curve output by the second curve model is the stiffness frequency response curve.

[0092] The first curve model and the second curve model can be models used to output the corresponding curves. The first curve model and the second curve model can be obtained through multi-scale analysis based on different parameters in the parameter set.

[0093] Multi-scale analysis refers to introducing multiple scales (such as time, space, or frequency) into the modeling and analysis process to more accurately describe the complex behavior of the system. In a hydraulically mounted system, the motion equations of the hydraulic components are first established. Then, multiple time scales are introduced into these equations using the multi-scale method, thereby constructing a nonlinear ordinary differential equation that reflects the fast and slow dynamic responses of the system. Solving this equation yields the constraint equations. Substituting the actual parameter set affecting the stiffness of the hydraulic mount into these equations, the first curve model used to generate the frequency response curve is finally derived.

[0094] In this embodiment of the application, the method for generating the first curve model may include:

[0095] Determine the equations of motion for the hydraulic components in a hydraulic suspension system;

[0096] By introducing multiple time scales into the motion equations of hydraulic components in a hydraulic suspension, nonlinear ordinary differential equations are obtained.

[0097] The constraint equations are obtained by solving the nonlinear ordinary differential equations.

[0098] Substituting the parameter set affecting the stiffness of the hydraulic suspension into the constraint equation, the first curve model is obtained.

[0099] The equations of motion for the hydraulic components in a hydraulic mount can be:

[0100]

[0101] in, For the position of hydraulic components, For the displacement of hydraulic components, Let be the acceleration of the hydraulic component, and m be the equivalent mass of the hydraulic component. For the fluid damping of hydraulic components, The reaction force generated by the hydraulic components.

[0102] The specific expression is: ;

[0103] in, For nonlinear damping, For secondary damping of the fluid, the reaction force of the hydraulic component can be expressed as:

[0104]

[0105] in, Position of hydraulic components; The external excitation angular velocity; For time; This refers to the gap between hydraulic components and rubber components. For external compression stroke;

[0106] As a correction factor, it satisfies: ;in, For nonlinear correction coefficients, The original correction factor. This refers to the actual effective stroke of the hydraulic component (i.e., the displacement relative to the initial position).

[0107] In this embodiment of the application, the inherent frequency Dimensionless time scale and feature length scale They respectively satisfy:

[0108]

[0109] ;

[0110] ;

[0111] By making the natural frequency, dimensionless time scale, and characteristic length scale dimensionless, we can obtain the dimensionless parameters:

[0112]

[0113]

[0114] Substituting the dimensionless parameters into the equation of motion of the hydraulic components in the hydraulic mount, we can obtain:

[0115] ;

[0116] in, ; , For external excitation angular velocity, This is nonlinear damping. Therefore, the parameters can be transformed to a dimensionless space, avoiding instability caused by excessive differences in magnitude between values. This is especially true for dimensionless time, which can solve numerical problems caused by high-frequency oscillations.

[0117] Among them, there are For external compression stroke, This refers to the gap between the hydraulic component and the rubber component.

[0118] Therefore, by rearranging the above equations of motion, we can obtain:

[0119] ;

[0120] in, , is a small parameter, This is the equivalent stiffness coefficient; for Compared to The first derivative; for Compared to The second derivative;

[0121] .

[0122] The equations obtained above are solved using the multi-scale method, thereby calculating the amplitude-frequency response curve and the phase-frequency response curve, where:

[0123] For forms such as: The equation, where For small parameters, multiple time scales are introduced: .

[0124] Since higher-order timescales are slower than lower-order timescales, the derivative with respect to time can be expanded as follows:

[0125] ;

[0126]

[0127] ;

[0128] in, For time scale The partial derivatives, For time scale The partial derivatives, For time scale The partial derivatives, The sign of the partial derivative.

[0129] Therefore, the solution can be expanded into a series:

[0130] .

[0131] in, The magnitude of the correction is the first power of the order of epsilon; The order of the correction is represented by the square root, and so on, up to the nth power of the correction (which is O).

[0132] Will:

[0133] ,

[0134] Substitute:

[0135] Then, aligning the equations of each order, we have:

[0136] ;

[0137] For the above formula, first solve the 0th-order equation, and we have an analytical solution:

[0138] ;

[0139] in, The imaginary unit, Let be the amplitude function (complex number), representing the amplitude over a time scale. The complex amplitude, the magnitude and phase of which vary with time scale Slow evolution, for The conjugate of complex numbers; Indicates time scale Oscillating and time-scale Slowly varying complex amplitude components, which contain phase information of the vibration; for The conjugate of complex numbers.

[0140] Considering , To characterize the dimensionless natural angular frequency, then:

[0141] ;

[0142] Therefore, combining the above-mentioned aligned equations of various orders, we have:

[0143] ;

[0144] in, Amplitude function right The first derivative; Conjugate amplitude right The second derivative; This refers to the residual part of the nonlinear or externally excited term after expansion.

[0145] Considering the forced incentive, satisfy:

[0146] ;

[0147] Therefore, as time progresses, the amplitude tends towards infinity, and the resulting constraint equations satisfy:

[0148]

[0149] in, Indicates and The residual function corresponding to the relevant oscillation term.

[0150] Solving this equation yields the frequency response curve. The standard test equation, the Duffing equation, is used as an example:

[0151]

[0152]

[0153] in, Characterizes the dimensionless natural angular frequency; The external excitation angular velocity;

[0154] The coefficient representing the cosine term; The coefficient representing the sin term;

[0155] To externally excite angular velocity steady-state vibration amplitude;

[0156] For nonlinear terms exist , The coefficient of the cosine term;

[0157] For nonlinear terms exist , The coefficient of the sin term.

[0158] If the external excitation angular velocity is set ,in The offset, representing the numerical value used to prevent numerical disturbances caused by different angular velocities, can be expressed by its stiffness frequency response curve as follows:

[0159] ;

[0160] The phase frequency response curve can be expressed as:

[0161] .

[0162] Based on this, by pre-deriving the multi-scale closed-loop solution of the hydraulic suspension system and embedding it in an FPGA, the corresponding stiffness and phase frequency response curves for each individual in the population can be quickly generated during subsequent optimization. This avoids the large computational overhead of traditional numerical simulations or frequency sweep experiments, significantly improving optimization efficiency and real-time performance. The system can then complete response adjustments to complex operating conditions within milliseconds, thereby achieving efficient and precise control of the hydraulic suspension performance.

[0163] In this embodiment, the first curve model and the second curve model can be preset in the processor. When the processor obtains the parameter set that affects the stiffness of the hydraulic suspension, it can directly substitute the parameters in the parameter set into the model to obtain the first frequency response curve and the second frequency response curve output by the first curve model and the second curve model.

[0164] In this embodiment, when the parameter set includes the equivalent mass, fluid damping, phase angle, and external compression stroke of the hydraulic components in the hydraulic mount, the parameter set affecting the stiffness of the hydraulic mount is input into the first curve model to obtain the first frequency response curve, including:

[0165] Based on the equivalent mass, fluid damping, phase angle, and external compression stroke of the hydraulic components in the hydraulic mount, determine the response parameters of the hydraulic components in the hydraulic mount. The response parameters shall include at least one of the following parameters: phase difference, amplitude, offset, nonlinear stiffness coefficient, damping parameter, and excitation force amplitude.

[0166] The response parameters of the hydraulic components in the hydraulic suspension are input into the first curve model to obtain the first frequency response curve output by the first curve model.

[0167] When the first frequency response curve output by the first curve model is a stiffness frequency response curve, the response parameters of the hydraulic components in the hydraulic suspension are determined based on the equivalent mass, fluid damping, phase angle, and external compression stroke of the hydraulic components in the hydraulic suspension. The response parameters include: amplitude, offset, nonlinear stiffness coefficient, damping parameter, and excitation force amplitude.

[0168] When the first frequency response curve output by the first curve model is a phase frequency response curve, the response parameters of the hydraulic components in the hydraulic suspension are determined based on the equivalent mass, fluid damping, phase angle, and external compression stroke of the hydraulic components in the hydraulic suspension. The response parameters include: phase difference, amplitude, offset, nonlinear stiffness coefficient, damping parameter, and excitation force amplitude.

[0169] In this context, phase difference can refer to the angular difference between the output response (such as displacement, velocity, or acceleration) and the input excitation (such as external force or displacement excitation) in a vibration system, corresponding to the time lag.

[0170] Amplitude refers to the maximum deviation of the output response (such as displacement, velocity, or acceleration) under periodic excitation. The magnitude of amplitude directly reflects the vibration intensity of the system after excitation. In vibration isolation systems, it is often used to measure the vibration damping capability of the suspension. The smaller the amplitude, the better the vibration isolation performance.

[0171] Offset can refer to a physical quantity used to describe how fast an excitation or response signal repeats.

[0172] Nonlinear stiffness coefficient can refer to a parameter that reflects the characteristics of stiffness as a function of displacement. It is used to describe the nonlinear relationship between stiffness and deformation, where stiffness is no longer a constant value.

[0173] Damping parameters are physical quantities used to characterize the energy dissipation capacity of a system, typically including viscous damping coefficients or equivalent damping ratios. Damping parameters determine the speed at which a system recovers to a stable state after being disturbed. Larger damping helps suppress resonance peaks and accelerates vibration decay, directly affecting vibration isolation and response stability in suspended systems.

[0174] The amplitude of the excitation force can refer to the maximum value of the external periodic excitation force applied to the system.

[0175] In this embodiment of the application, when the first frequency response curve is a stiffness frequency response curve, the first curve model satisfies:

[0176] ;

[0177] in, For amplitude, For nonlinear stiffness coefficients, The damping coefficient is... This is the offset. , and It is a dimensionless number. The magnitude of the external force.

[0178] In this embodiment of the application, when the first frequency response curve is a phase frequency response curve, the first curve model satisfies:

[0179] ;

[0180] in, The phase angle, For amplitude, This is the offset. For nonlinear stiffness coefficients, The damping coefficient is... , and It is a dimensionless number. The magnitude of the external force.

[0181] Therefore, by applying the pre-derived and embedded multi-scale closed-loop solution in the FPGA to the actual optimization and control process, the system only needs to input the current parameter set (such as equivalent mass, fluid damping, nonlinear stiffness coefficient, etc.) during runtime to generate the corresponding stiffness frequency response curve and phase frequency response curve in milliseconds. This eliminates the need for traditional numerical simulations or frequency sweep experiments, significantly improving computational efficiency and system response speed. It enables rapid modeling and real-time prediction of the dynamic characteristics of hydraulic suspensions, providing efficient and stable technical support for subsequent online optimization and adaptive control.

[0182] Multi-objective optimization results refer to the results obtained by comprehensively considering multiple mutually restrictive performance indicators in a hydraulic suspension system (such as low-frequency vibration isolation capability, resonance peak suppression capability, high-frequency stiffness characteristics, phase stability, etc.), utilizing the dynamic response information provided by the first frequency response curve (such as stiffness frequency response curve) and the second frequency response curve (such as phase frequency response curve), and coordinating the adjustment of key parameters affecting system performance (such as equivalent mass, fluid damping, nonlinear stiffness coefficient, etc.) through optimization algorithms, ultimately obtaining a set of Pareto optimal solutions.

[0183] S204. Adjust the response parameters of the hydraulic suspension based on the multi-objective optimization results.

[0184] The optimal design parameters (such as equivalent mass, fluid damping coefficient, nonlinear stiffness coefficient, etc.) obtained from multi-objective optimization are substituted into the dynamic model of the hydraulic suspension system. The dynamic response of the system under different excitation frequencies is recalculated or simulated, thereby updating its response parameters (such as amplitude, phase difference, equivalent stiffness, damping ratio, etc.). After obtaining the required parameter set, the actuators, active components, etc. adjust stiffness, displacement, rotation angle, etc. according to the parameter identification results to achieve quasi-zero stiffness across a wide range and frequency band. This makes the actual output characteristics of the system more in line with the performance requirements set in the optimization objectives, and ultimately achieves precise control of the dynamic behavior of the hydraulic suspension and overall performance optimization.

[0185] The hydraulic suspension control method provided in this application embodiment can quickly select the optimal parameter combination according to the actual working conditions. By using the built-in first curve model and second curve model, it skips the frequency sweeping process required for traditional numerical parameter identification, improves the calculation speed to the millisecond level, and realizes real-time response to changes in working conditions. As a result, it not only significantly improves the vibration isolation performance of the hydraulic suspension in a wide frequency band, but also enhances the system's adaptability to complex driving environments. At the same time, by optimizing the compliance of the support stiffness, it effectively reduces the structural sound transmission path, thereby comprehensively improving the vehicle's NVH performance.

[0186] Figure 3 Flowchart of the control method for the hydraulic suspension provided in this application Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2Based on the embodiments, the steps for performing multi-objective optimization on the parameter set according to the first frequency response curve and the second frequency response curve to obtain the multi-objective optimization result are described in detail. The method includes:

[0187] S301. Based on the differences between corresponding points on the first and second frequency response curves, construct an optimization function for multi-objective optimization;

[0188] S302. Based on the optimization function for multi-objective optimization, perform multi-objective optimization on the parameter set to obtain the multi-objective optimization result.

[0189] In this embodiment of the application, an optimization function for multi-objective optimization is constructed based on the differences between corresponding points on the first and second frequency response curves, including:

[0190] Based on the differences between corresponding points on the first and second frequency response curves, a mean square error curve is generated.

[0191] Based on the mean squared error curve, a cost function is generated to represent the optimization function used for multi-objective optimization.

[0192] The optimization function for multi-objective optimization can refer to a mathematical expression used to describe the trade-offs between multiple performance objectives. In the embodiments of this application, the optimization function is constructed based on the differences between the stiffness frequency response curve and phase frequency response curve of the hydraulic suspension system and the target response, aiming to simultaneously optimize multiple conflicting objectives (such as improving vibration isolation efficiency, reducing resonance peak height, enhancing system stability, etc.), thereby guiding the parameter set to evolve towards the optimal solution.

[0193] The optimization function for multi-objective optimization can be a cost function generated according to the definition of the mean square error (MSE) curve. This cost function can be a quantitative indicator that measures the deviation between the system's current output and the ideal target. Specifically, it is formed by comparing the actual response values ​​at each frequency point on the stiffness and phase frequency response curves with the target response values, calculating the squared error, and averaging the results to form the MSE form of the cost function. The smaller the value of this function, the closer the system response is to the desired target.

[0194] The mean square error (MSE) curve is constructed from the differences between corresponding points on the stiffness and phase frequency response curves. This MSE curve is the error distribution curve formed after calculating the sum of squares of stiffness and phase errors at each frequency point and normalizing it across the entire frequency range. The MSE curve not only reflects the degree of matching between the overall system response and the objective, but also provides a quantifiable performance evaluation standard for subsequent multi-objective optimization, thereby facilitating coordinated control of different objectives during the optimization process.

[0195] The process of performing multi-objective optimization on the parameter set based on the optimization function to obtain the multi-objective optimization result can include executing the NSGA algorithm, performing crossover, mutation, and selecting elite populations, etc. This process may include:

[0196] First, a population with multiple parameter combinations is initialized, each parameter group representing a set of possible hydraulic suspension system configurations. Then, a pre-trained closed analytical model is used to quickly generate stiffness and phase frequency response curves for each parameter group, and the corresponding cost function value is calculated based on the difference between these curves and the target curve as one of the optimization objectives.

[0197] Then, during each generation of evolution, the NSGA algorithm performs crossover operations on individuals in the current population, simulating biological genetic mechanisms to generate new parameter combinations; it then introduces small perturbations through mutation operations to increase population diversity and avoid getting trapped in local optima. Afterwards, the entire population after merging parents and offspring is subjected to non-dominated ranking and crowding assessment to select high-quality individuals on the Pareto front.

[0198] Finally, through iterative optimization combined with an elite retention strategy, the optimal solution set is gradually approximated, yielding a set of parameters that achieves the best balance among multiple performance objectives. This enables efficient and high-precision optimization of the dynamic characteristics of hydraulic suspension systems without relying on complex numerical simulations and frequency sweep experiments, significantly improving their robustness and adaptability in practical applications.

[0199] Figure 3a This is a schematic diagram illustrating the descent effect of the cost objective function in parameter identification provided in the embodiments of this application, as shown below. Figure 3a As shown in the figure, the performance of different parameter combinations on the three optimization objectives of phase alignment, frequency alignment, and amplitude alignment is illustrated. As the optimization iteration proceeds, the cost values ​​corresponding to each parameter group gradually decrease, showing a trend of the scatter points moving closer to the origin of the coordinate system (i.e., the ideal matching state). This indicates that the difference between the system response and the target curve is continuously decreasing, and the optimization effect is significantly improved.

[0200] The hydraulic suspension control method provided in this application generates a mean square error curve based on the differences between frequency points on the stiffness frequency response curve and the phase frequency response curve. A cost function for multi-objective optimization is then constructed based on this mean square error curve. This allows for the quantification of the deviation between the system response and the target performance within a unified mathematical framework, thereby guiding the optimization algorithm to effectively weigh multiple conflicting objectives. This not only improves the accuracy and convergence efficiency of the optimization process but also enables refined control of the dynamic characteristics of the hydraulic suspension (such as vibration isolation capability and phase stability).

[0201] Figure 4A schematic diagram of the control device for the hydraulic suspension provided in this application is shown below. Figure 4 As shown, the hydraulic suspension control device 40 provided in this embodiment includes:

[0202] The acquisition module 401 is used to acquire a set of parameters that affect the stiffness of the hydraulic suspension.

[0203] The first module 402 is used to input the parameter set that affects the stiffness of the hydraulic suspension into the first curve model to obtain the first frequency response curve.

[0204] The second obtaining module 403 is used to perform multi-objective optimization on the parameter set based on the first frequency response curve and the second frequency response curve to obtain the multi-objective optimization result; the second frequency response curve is the curve output by the second curve model, and both the first curve model and the second curve model are models obtained by multi-scale analysis of the parameter set; wherein, when the first frequency response curve output by the first curve model is the stiffness frequency response curve, the second frequency response curve output by the second curve model is the phase frequency response curve, and when the first frequency response curve output by the first curve model is the phase frequency response curve, the second frequency response curve output by the second curve model is the stiffness frequency response curve;

[0205] Adjustment module 404 is used to adjust the response parameters of the hydraulic suspension based on the multi-objective optimization results.

[0206] In one possible implementation, the parameter set includes the equivalent mass of the hydraulic components in the hydraulic mount, fluid damping, phase angle, and external compression stroke;

[0207] The first module 402 can also be specifically used for:

[0208] Based on the equivalent mass, fluid damping, phase angle, and external compression stroke of the hydraulic components in the hydraulic mount, determine the response parameters of the hydraulic components in the hydraulic mount. The response parameters shall include at least one of the following parameters: phase difference, amplitude, offset, nonlinear stiffness coefficient, damping parameter, and excitation force amplitude.

[0209] The response parameters of the hydraulic components in the hydraulic suspension are input into the first curve model to obtain the first frequency response curve output by the first curve model.

[0210] In one possible implementation, when the first frequency response curve is a stiffness frequency response curve, the first curve model in the first obtaining module 403 satisfies:

[0211] ;

[0212] in, For amplitude, For nonlinear stiffness coefficients, The damping coefficient is... This is the offset. , and It is a dimensionless number. The magnitude of the external force.

[0213] In one possible implementation, when the first frequency response curve is a phase frequency response curve, the first curve model in the first obtaining module 402 satisfies:

[0214] ;

[0215] in, The phase angle, For amplitude, This is the offset. For nonlinear stiffness coefficients, The damping coefficient is... , and It is a dimensionless number. The magnitude of the external force.

[0216] In one possible implementation, the first obtaining module 402 may also be specifically used for:

[0217] Determine the equations of motion for the hydraulic components in a hydraulic suspension system;

[0218] By introducing multiple time scales into the motion equations of hydraulic components in a hydraulic suspension, nonlinear ordinary differential equations are obtained.

[0219] The constraint equations are obtained by solving the nonlinear ordinary differential equations.

[0220] Substituting the parameter set affecting the stiffness of the hydraulic suspension into the constraint equation, the first curve model is obtained.

[0221] In one possible implementation, the adjustment module 404 can also be specifically used for:

[0222] Based on the differences between corresponding points on the first and second frequency response curves, an optimization function for multi-objective optimization is constructed.

[0223] Based on the optimization function used for multi-objective optimization, multi-objective optimization is performed on the parameter set to obtain the multi-objective optimization result.

[0224] In one possible implementation, the adjustment module 404 can also be specifically used for:

[0225] Based on the differences between corresponding points on the first and second frequency response curves, a mean square error curve is generated.

[0226] Based on the mean squared error curve, a cost function is generated to represent the optimization function used for multi-objective optimization.

[0227] In one possible implementation, obtaining a set of parameters affecting the stiffness of the hydraulic suspension includes:

[0228] The excitation signal in the road surface excitation is obtained, which is obtained by the sensor detecting the road surface or by the simulation results of the road surface in the simulation system;

[0229] The design optimization objective is determined based on the excitation signal in the road surface excitation.

[0230] Based on the design optimization objectives, obtain the parameter set that affects the stiffness of the hydraulic suspension.

[0231] The hydraulic suspension control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0232] Figure 5 This is a structural schematic diagram of the hydraulic suspension device provided in this application. Figure 5 As shown, the hydraulic suspension device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0233] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0234] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0235] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0236] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0237] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0238] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0239] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0240] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0241] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0242] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0243] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0244] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0245] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0246] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0247] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of controlling a hydraulic mount, characterized by, The method is applied to a processor installed in the hydraulic suspension, and comprises the following steps: obtaining a parameter group affecting the stiffness of the hydraulic suspension, the parameter group comprising an equivalent mass, a liquid damping, a phase angle and an external compression stroke of a hydraulic element in the hydraulic suspension; determining a response parameter of the hydraulic element in the hydraulic suspension according to the equivalent mass, the liquid damping, the phase angle and the external compression stroke of the hydraulic element in the hydraulic suspension, the response parameter at least comprising one of a phase difference, an amplitude, an offset, a nonlinear stiffness coefficient, a damping parameter and an excitation force amplitude; inputting the response parameter of the hydraulic element in the hydraulic suspension into a first curve model to obtain a first frequency response curve output by the first curve model; constructing an optimization function for multi-objective optimization according to a difference between corresponding points on the first frequency response curve and a second frequency response curve, performing multi-objective optimization on the parameter group according to the optimization function for multi-objective optimization to obtain a multi-objective optimization result, the second frequency response curve being a curve output by a second curve model preset, the first curve model and the second curve model both being models obtained by multi-scale analysis on the parameter group, wherein when the first frequency response curve output by the first curve model is a stiffness frequency response curve, the second frequency response curve output by the second curve model is a phase frequency response curve, and when the first frequency response curve output by the first curve model is a phase frequency response curve, the second frequency response curve output by the second curve model is a stiffness frequency response curve; adjusting the response parameter of the hydraulic suspension according to the multi-objective optimization result; the step of constructing the optimization function for multi-objective optimization according to the difference between the corresponding points on the first frequency response curve and the second frequency response curve comprises: generating a mean square error curve according to the difference between the corresponding points on the first frequency response curve and the second frequency response curve; generating a cost function representing the optimization function for multi-objective optimization according to the mean square error curve.

2. The method of claim 1, wherein, when the first frequency response curve is the stiffness frequency response curve, the first curve model satisfies: ； wherein the is the amplitude, the is the nonlinear stiffness coefficient, the is the damping coefficient, the is the offset, the , the , and the is the dimensionless number, the is the amplitude of the external force.

3. The method of claim 1, wherein, when the first frequency response curve is the phase frequency response curve, the first curve model satisfies: ； Wherein, the is a phase angle, the is an amplitude, the is an offset, the is a nonlinear stiffness coefficient, the is a damping coefficient; The , the , and the are dimensionless numbers, and the is the amplitude of the external force.

4. The method according to any one of claims 1 to 3, characterized in that, the method further comprises: determining a motion equation of the hydraulic element in the hydraulic suspension; introducing a plurality of time scales into the motion equation of the hydraulic element in the hydraulic suspension to obtain a nonlinear ordinary differential equation; solving the nonlinear ordinary differential equation to obtain a constraint equation; substituting the parameter group affecting the stiffness of the hydraulic suspension into the constraint equation to obtain the first curve model.

5. The method of claim 1, wherein, the step of obtaining the parameter group affecting the stiffness of the hydraulic suspension comprises: obtaining an excitation signal in a road excitation, the excitation signal being obtained by a sensor detecting a road or obtained according to a simulation result of a simulation system simulating a road; determining a design optimization target according to the excitation signal in the road excitation; obtaining the parameter group affecting the stiffness of the hydraulic suspension according to the design optimization target.

6. A control device for a hydraulic mount, characterized by ​ An acquisition module is configured to acquire a parameter group affecting a hydraulic suspension stiffness, the parameter group including an equivalent mass, a liquid damping, a phase angle, and an external compression stroke of a hydraulic element in the hydraulic suspension. A first obtaining module is configured to determine a response parameter of the hydraulic element in the hydraulic suspension according to the equivalent mass, the liquid damping, the phase angle, and the external compression stroke of the hydraulic element in the hydraulic suspension, the response parameter including at least one of a phase difference, an amplitude, an offset, a nonlinear stiffness coefficient, a damping parameter, and an excitation force amplitude; input the response parameter of the hydraulic element in the hydraulic suspension into a first curve model to obtain a first frequency response curve output by the first curve model. A second obtaining module is configured to construct an optimization function for multi-objective optimization according to a difference between corresponding points on the first frequency response curve and a second frequency response curve; perform multi-objective optimization on the parameter group according to the optimization function for multi-objective optimization to obtain a multi-objective optimization result; the second frequency response curve is a curve output by a second curve model pre-set, and the first curve model and the second curve model are models obtained by multi-scale analysis on the parameter group; when the first frequency response curve output by the first curve model is a stiffness frequency response curve, the second frequency response curve output by the second curve model is a phase frequency response curve; when the first frequency response curve output by the first curve model is a phase frequency response curve, the second frequency response curve output by the second curve model is a stiffness frequency response curve. An adjustment module is configured to adjust the response parameter of the hydraulic suspension according to the multi-objective optimization result. The second obtaining module is specifically configured to generate a mean square error curve according to a difference between corresponding points on the first frequency response curve and the second frequency response curve; and generate a cost function representing the optimization function for multi-objective optimization according to the mean square error curve.

7. A hydraulic mount apparatus characterized by comprising: comprise: a memory, a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, so that the processor executes the method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1-5.

9. A computer program product, characterised in that, The computer program is executed by the processor to implement the method in any one of claims 1-5.

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