Self-adaptive control and state estimation method for semi-active suspension of vehicle

Abstract

The invention discloses a self-adaptive control and state estimation method for a semi-active suspension of a vehicle, and the method comprises the steps: determining a control strategy of mixed VBGH-ABGH through calculating a stability crossover frequency, and improving the control stability in an excitation frequency band of a whole road surface; according to the comfort-oriented SH-ADD algorithm based on the comfort cross frequency, real-time switching between the SH algorithm and the ADD algorithm in the control process can be ensured, so that the SH-ADD algorithm has good adaptivity and an optimal control effect; the engineering realizability of the control method is ensured by an on-line real-time estimation method of the vehicle suspension state variable based on FFT (Fast Fourier Transform); a self-adaptive control method for a semi-active suspension of a vehicle comprises the steps that an SH-ADD algorithm facing comfort and a VBGH-ABGH algorithm facing operation stability serve as a bottom-layer controller, an upper-layer controller is adopted, the two algorithms in the bottom-layer controller are switched in real time based on calculation of the transverse acceleration of the vehicle, the driving working condition is considered, and the driving stability of the vehicle is improved. And the optimality of two policies at the bottom layer is fully considered without sacrificing the performance of a certain aspect.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent suspension of vehicles, and in particular relates to a method for adaptive control and state estimation of a semi-active suspension of a vehicle. Background Art

[0002] Semi-active suspension has become a research hotspot in the field of intelligent suspension in vehicle engineering in recent years. With the increasing maturity of controlled damping (CDC) and MRC shock absorbers, intelligent suspension systems centered on semi-active suspension technology are gradually being engineered and mass-produced.

[0003] Semi-active suspension systems consist of controlled damping shock absorbers and a control system whose key technology is a control algorithm. Regarding controlled damping shock absorber structures, the main manufacturers of CDC are Germany's Sachs, the United States' Bilstein, and Monroe; the main manufacturer of MRC is Beijing West Heavy Industry. However, these companies have not disclosed the core control algorithms.

[0004] In recent years, some domestic manufacturers have begun mass production of controllable damping shock absorber structures. However, these models are still primarily focused on typical operating conditions, such as controlling pitch during braking and starting, and controlling roll during cornering. Due to the difficulties in implementing fully automatic control algorithms and engineering approaches, OEMs are currently focusing on research and development.

[0005] The difficulty of the control algorithm lies in the contradiction between comfort and handling stability, as well as the contradiction between the two in different road excitation frequency bands. The other difficulty lies in engineering feasibility.

[0006] Currently well-known in the engineering community is the skyhook damping algorithm (SH), proposed by American Professor Karnopp. It is a comfort-oriented control strategy. Its characteristic is that it significantly improves the sprung mass vibration acceleration in the low-frequency band around the sprung mass resonance point, but worsens the vibration acceleration in the mid- and high-frequency bands. Another algorithm, acceleration-driven damping (ADD), is the opposite of SH, worsening the vibration acceleration in the low-frequency band but significantly improving it in the mid- and high-frequency bands. In 2007, Professor Savaresi of Imperial University of London proposed the mixed SH-ADD algorithm, which combines the advantages of both algorithms and switches between them through the calculation of crossover frequencies.

[0007] However, both SH and SH-ADD are aimed at improving comfort, specifically by reducing the vibration acceleration of the sprung mass. To improve vehicle handling stability, Cole et al. proposed the ground-hook damping algorithm (GH) in 1994. This algorithm is similar to SH, so this algorithm is referred to as VBGH (velocity-based ground-hook) in this paper. VBGH is oriented toward vehicle stability, aiming to reduce dynamic wheel loads. In 2020, Kopylovet proposed the acceleration-based ground-hook algorithm (ABGH), drawing on ADD.

[0008] The engineering implementation of the aforementioned SH-ADD, VBGH, and ABGH algorithms all require absolute and relative velocity signals of the vehicle's sprung and unsprung masses, using these state variable signals to perform damping switching. However, measuring these signals on a vehicle is extremely difficult. Using Kalman filtering is computationally intensive, sensitive to model accuracy, and difficult to implement in non-static environments. Summary of the Invention

[0009] In view of this, the present invention provides a method for adaptive control and state estimation of a vehicle semi-active suspension, aiming to provide a technical solution of a control algorithm for the engineering production of a vehicle semi-active intelligent suspension.

[0010] A vehicle semi-active suspension adaptive control method includes: when the road surface excitation dominant frequency f excitation When the dominant frequency of the road excitation is greater than the set crossover frequency Ω, the VBGH control algorithm is used to control the vehicle.

[0011] Preferably, the calculation method of the set crossover frequency Ω includes:

[0012] Maintain the tire stiffness K of each wheel w In the range of the mass on each wheel spring, the mass on the spring m s The value increases from the minimum value to the maximum value with the set step length, and the value of the mass on each wheel spring at each incremental order is obtained: m s =[m s,1 m s,2 …m s,N ]; where N is the increment order;

[0013] Increase the tire stiffness of each wheel within the value range from the minimum value to the maximum value with a set step size, and obtain the value of the tire stiffness of each wheel at each incremental order: K w =[K w,1 K w,2 …Kw,N ].

[0014] For wheel i, the mass on the sprung wheel is m s , tire stiffness k w Under the condition of VBGH control algorithm and ABGH algorithm, the frequency response analysis is carried out to obtain the crossover frequency Ω for stability. n,h Traverse n = 1, ..., N, h = 1, ..., N, and obtain the stability-oriented cross-frequency for each combination of sprung mass and tire stiffness, and obtain the N × N-dimensional stability-oriented cross-frequency matrix:

[0015]

[0016] When switching between the ABGH and VBGH control algorithms, the crossover frequency corresponding to the vehicle's current sprung mass and tire stiffness data is found in the stability-oriented crossover frequency matrix. This frequency is then compared with the dominant frequency of the road excitation to complete the switch between the two algorithms.

[0017] Preferably, the tire stiffness k w Calculation based on tire pressure sensor:

[0018]

[0019] Where P is tire pressure, W is tire width, and D is tire diameter;

[0020] sprung mass m s Calculated by the following formula:

[0021]

[0022] Where g represents the acceleration due to gravity, k b is the suspension stiffness; Δs is the suspension deformation at static equilibrium, which is obtained through the dynamic displacement sensor or angle sensor installed on the intelligent suspension vehicle.

[0023] Preferably, the dominant frequency of the road excitation f excitation Obtained using the following method:

[0024] Collect vertical acceleration sensor signals at the wheel axis After performing FFT, first divide it by the number of points N, then remove the periodicity, and finally take the modulus of the signal. The largest modulus is the dominant frequency f of the road excitation. excitation .

[0025] Preferably, the crossover frequency is updated each time the vehicle is started.

[0026] A vehicle semi-active suspension adaptive control method is used to control the vehicle's comfort when the dominant frequency of the road excitation is fexcitation When the road surface excitation dominant frequency is greater than the set crossover frequency α, the vehicle is controlled using the ADD control algorithm. The calculation method of the crossover frequency α includes:

[0027] Maintain the tire stiffness K of each wheel w In the range of the mass on each wheel spring, the mass on the spring m s The value increases from the minimum value to the maximum value with the set step length, and the value of the mass on each wheel spring at each incremental order is obtained: m s =[m s,1 m s,2 …m s,N ]; where N is the increment order;

[0028] Increase the tire stiffness of each wheel within the value range from the minimum value to the maximum value with a set step size, and obtain the value of the tire stiffness of each wheel at each incremental order: K w =[K w,1 K w,2 …K w,N ];

[0029] For wheel i, the mass on the sprung wheel is m s , tire stiffness k w Under the condition of SH algorithm and ADD algorithm, the frequency response analysis is carried out to obtain the comfort-oriented crossover frequency α n,h Traverse n = 1, ..., N, h = 1, ..., N, and obtain the comfort-oriented cross-frequency for each combination of sprung mass and tire stiffness, and obtain an N × N-dimensional comfort-oriented cross-frequency matrix:

[0030]

[0031] Based on the vehicle's current sprung mass and tire stiffness data, the crossover frequency corresponding to the current sprung mass and tire stiffness data is found in the comfort-oriented crossover frequency matrix, and the SH algorithm and ADD algorithm are switched based on this crossover frequency.

[0032] Preferably, the state variable estimation method required for vehicle control includes:

[0033] (1) When the vehicle is started, a sliding window is used to sample the acceleration signals of the sprung mass and the unsprung mass. The interval between adjacent sliding windows is ΔN sampling points. The width of each sliding window is N, and the sequence number of the sliding window is k. The sampling is performed once every 1 ms, and the sliding window width is N = 1024 sampling points. The acceleration signal Stored in a buffer, where n = 0, 1, ..., N-1;

[0034] (2) After completing a sliding window sampling, the acceleration signal is low-pass filtered;

[0035] (3) Acceleration signal after low-pass filtering Perform FFT transformation to obtain the time domain acceleration signal A k [ω]; where ω represents frequency; A k [ω] represents the acceleration signal corresponding to the kth sliding window;

[0036] (4) After FFT transformation, the frequency domain signal A k [ω] The frequency components below 1 Hz are set to zero amplitude;

[0037] (5) Then, the frequency domain integration theory is applied to the signal obtained in step (4) to perform frequency domain integration processing to obtain the frequency domain velocity signal:

[0038]

[0039] (6) The frequency domain signal of the velocity is obtained by IFFT to obtain the time domain signal of the velocity:

[0040]

[0041] (7) When k ≥ 1, during the frequency domain integration process, the initial value of this integration is set to the integral value at ΔN obtained in the previous sliding window, which is equivalent to adding this value as an offset increment to the current integration data. The corrected velocity is obtained by the following formula:

[0042]

[0043] (8) After every ΔN sampling points, the sliding window moves once, and the process from the second step (2) to the eighth step (8) is repeated, thereby realizing the online estimation process of the speed state.

[0044] A vehicle semi-active suspension adaptive control method utilizes an upper-layer controller to switch between a hybrid SH-ADD control algorithm and a hybrid VBGH-ABGH control algorithm according to lateral acceleration to achieve vehicle control. Specifically, when the lateral acceleration is less than a set threshold, the hybrid SH-ADD control algorithm is used; otherwise, the hybrid VBGH-ABGH control algorithm is used.

[0045] Among them, the hybrid VBGH-ABGH control algorithm is used, when the dominant frequency of the road excitation f excitation When the dominant frequency of the road excitation is greater than the set crossover frequency Ω, the VBGH control algorithm is used to control the vehicle.

[0046] In the hybrid SH-ADD control algorithm, when the dominant frequency of the road excitation f excitation When the dominant frequency of the road excitation is greater than the set crossover frequency α, the ADD control algorithm is used to control the vehicle.

[0047] Preferably, the calculation formula of lateral acceleration is as follows:

[0048]

[0049] Wherein, L is the vehicle wheelbase; K us is the understeer coefficient, v is the vehicle speed, and δ is the steering wheel angle.

[0050] The present invention has the following beneficial effects:

[0051] The intelligent suspension technology to which this invention relates represents a development direction in vehicle engineering and even intelligent vibration reduction technology. It is urgently needed in the current intelligent vehicle technology landscape and offers broad prospects for engineering production. The beneficial effect of this invention is that the proposed control algorithm for semi-active intelligent vehicle suspension can be implemented in an engineering context, resolving the control algorithm bottleneck that has hindered the implementation of semi-active intelligent vehicle suspension technology. The proposed algorithm also has potential for transplantation and application in other intelligent vibration reduction technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the single wheel vibration dynamic model of vehicle suspension;

[0053] Figure 2 is the frequency response between the hybrid SH-ADD vehicle acceleration and road excitation;

[0054] Figure 3 is the frequency response of tire deformation relative to road excitation;

[0055] Figure 4 It is the process of identifying the dominant frequency of the road surface;

[0056] Figure 5 is the simulation result of the identification of the dominant frequency of the road surface; Figure 5 (a) is the identification interval 2s-3s, Figure 5 (b) is the identification interval 6s-7s;

[0057] Figure 6 is the frequency response of the hybrid VBGH-ABGH algorithm;

[0058] Figure 7 is the tire deformation response, where Figure 7 (a) is 1.5Hz, Figure 7 (b) 10 Hz;

[0059] Figure 8 is the tire deformation on a D-grade road surface;

[0060] Figure 9 This is the principle diagram of the adaptive crossover frequency update mechanism;

[0061] Figure 10 Calibrate 2-D maps for comfort and stability crossover frequencies;

[0062] Figure 11 This is the implementation process of the online state estimation algorithm of the present invention;

[0063] Figure 12 This is a comparison chart between the online speed estimation simulation and the theoretical value;

[0064] Figure 13 It is the multi-objective control principle diagram of the present invention;

[0065] Figure 14 Comparison between the model calculated value of the lateral acceleration value of the present invention and the CARSIM simulation value;

[0066] Figure 15 This is the schematic diagram of the joint simulation structure of CarSim and Simulink;

[0067] Figure 16 This is the lane-changing test scenario of the present invention;

[0068] Figure 17 This is a simulation diagram of scenario 1 of the present invention;

[0069] Figure 18 This is the high-speed curved track of the present invention; wherein (a) is the race scene, (b) is the circular curved track, (c) is the Class B road surface profile, and (d) is the road surface height;

[0070] Figure 19 Here is a comparison picture of the curved track moment;

[0071] Figure 20 This is a comparison chart of curvy track performance. DETAILED DESCRIPTION

[0072] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0073] Comparative studies have shown that compared with passive suspension, ABGH can significantly improve operational stability in the low-frequency region, while VBGH's performance deteriorates in the low-frequency region. However, VBGH can reduce dynamic loads within the wheel's resonant frequency range, indicating that ABGH and VBGH exhibit different performance in different frequency bands.

[0074] Based on this discovery, the present invention proposes a hybrid VBGH-ABGH control strategy for handling stability. The goal is to improve handling stability across the entire road excitation frequency band. The VBGH-ABGH algorithm relies on accurately determining the stability crossover frequency, which is variable, changing with changes in occupant and tire stiffness. Therefore, the present invention proposes an adaptive calculation method for the stability crossover frequency.

[0075] Based on the hybrid SH-ADD algorithm, the present invention proposes a comfort-oriented SH-ADD algorithm based on comfort crossover frequency, which can ensure real-time switching between the SH algorithm and the ADD algorithm during the control process, thereby making the SH-ADD algorithm have good adaptability and optimal control effect.

[0076] The present invention also proposes an online real-time estimation and calculation method for vehicle suspension state variables based on FFT, thereby ensuring the engineering feasibility of the control method proposed by the present invention.

[0077] On the basis of the above invention contents, the present invention proposes a method for adaptive control of a semi-active suspension of a vehicle. This method uses the comfort-oriented SH-ADD algorithm and the handling stability-oriented VBGH-ABGH algorithm as the bottom-level controller, and adopts an upper-level controller to switch the two algorithms in the bottom-level controller in real time based on the calculation of the vehicle's lateral acceleration. When the lateral acceleration exceeds the threshold value, it means that there is a risk of sideslip. At this time, the upper-level controller switches the bottom-level controller to the VBGH-ABGH algorithm to control the vehicle; conversely, when the lateral acceleration is lower than the threshold value, the typical working condition is that the vehicle is driving in a straight line. At this time, the bottom-level controller works in the comfort-oriented SH-ADD algorithm. The algorithm proposed by the present invention takes into account the driving conditions, does not sacrifice the performance of any one aspect, and fully takes into account the optimality of the two bottom-level strategies.

[0078] The inventive method proposed in the present invention has been verified by Simulink simulation or CarSim and Simulink joint simulation. The proposed adaptive control method has been tested by simulating a real vehicle. Through simulation tests under various typical driving scenarios, it has been proved that the adaptive method has good performance under various different actual driving conditions.

[0079] First, we introduce the background algorithm related to the present invention:

[0080] 1.1. Vehicle Semi-Active Suspension Single-Wheel Two-Degree-of-Freedom Model

[0081] The single wheel vibration dynamics model of the vehicle semi-active suspension is as follows: Figure 1 shown.

[0082] according to Figure 1 , the dynamic equation is established as follows:

[0083]

[0084] where z b ,z w ,z r Represents the vertical absolute displacement of the vehicle body, wheels and road elevation respectively; m b 、m w , are the sprung mass and unsprung mass respectively. k b 、k w are the suspension and tire stiffness respectively; Δs and Δw represent the compression of the suspension spring and tire stiffness respectively when in static equilibrium; the first-order dynamics reflects the vibration damping at c in The delay relationship between the input and the actual output c(t), β is the modulation bandwidth of the semi-active suspension damper.

[0085] The parameters of the simulation verification model of the present invention are shown in Table 1.

[0086] Table 1. Simulation model parameters

[0087]

[0088] 1.2. Hybrid SH-ADD algorithm

[0089] Professor Karnopp proposed the following comfort-oriented ceiling damping algorithm:

[0090]

[0091] in, is the absolute displacement of the sprung mass; is the suspension dynamic speed.

[0092] The ADD control algorithm proposed by Professor Savaresi is as follows:

[0093]

[0094] in, is the acceleration of the sprung mass.

[0095] The hybrid SH-ADD algorithm proposed by Professor Savaresi of Italy is as follows:

[0096]

[0097] in, is the acceleration of the sprung mass; is the absolute velocity of the sprung mass, is the moving speed; α is the cross-over frequency of the hybrid SH-ADD algorithm (SH-ADD cross-over frequency).

[0098] The SH-ADD algorithm uses the following formula to determine the dominant frequency of road excitation f excitation Determination of crossover frequency:

[0099]

[0100] This judgment method does not require real-time estimation of the dominant road excitation frequency f excitation It is an indirect calculation and estimation method in the time domain. Its advantage is good real-time performance. However, this condition is indirectly derived under simple harmonic excitation conditions, and its specific effect on random pavement needs to be evaluated.

[0101] The hybrid SH-ADD algorithm proposed by Italian Professor Savaresi above shows that the switching between the SH and ADD algorithms is determined by a specific frequency point α, which is called the comfort-oriented crossover frequency in this invention. This compensates for the shortcomings of the SH and ADD algorithms in different frequency bands and fully utilizes the advantages of the two algorithms in different frequency bands. The frequency responses obtained by simulating the four algorithms of passive suspension, SH, ADD, and hybrid SH-ADD are shown in the figure below. Figure 2 .

[0102] from Figure 2 It can be seen that the vibration acceleration obtained by switching between SH and ADD according to the dominant frequency of road excitation by the hybrid SH-ADD is the smallest in the entire frequency band.

[0103] 1.3.VBGH and ABGH algorithms

[0104] Both VBGH and ABGH algorithms are heuristic algorithms, which are proposed with reference to SH and ADD algorithms.

[0105] The heuristic VBGH algorithm is as follows:

[0106]

[0107] in, is the absolute velocity of the unsprung mass.

[0108] The acceleration-based heuristic algorithm ABGH proposed by Kopylov is as follows:

[0109]

[0110] in, is the acceleration of the unsprung mass.

[0111] The following are the four main invention points of the present invention.

[0112] Example 1: Hybrid VBGH-ABGH algorithm

[0113] 2.1. Handling stability evaluation and comparative analysis of VBGH and ABGH performance

[0114] Hybrid SH-ADD improves vehicle body acceleration at the expense of wheel vibration. Wheel vibration affects dynamic loads, and when dynamic loads exceed static loads, wheel hop occurs, affecting handling stability. Reducing dynamic loads improves handling stability and enhances road friendliness.

[0115] When the tire deformation (z w -z r ) satisfies the following formula, a wheel jump will occur:

[0116]

[0117] The frequency response of the dynamic load (tire deformation) of the present invention is evaluated and calculated using the following formula, and the simulation results are shown in Figure 3 .

[0118]

[0119] In the above formula, N is the number of wheels;

[0120] from Figure 3 It can be seen that VBGH minimizes the dynamic load near the unsprung mass resonance point, but deteriorates in the low frequency range. In contrast, ABGH deteriorates the dynamic load near the unsprung mass resonance point, but improves it near the sprung mass resonance point. Note that when the dominant frequency of the road excitation is greater than the crossover frequency, Figure 3 It is not obvious that VBGH is better than ABGH. This is because the vibration near the unsprung mass resonance point is high-frequency vibration, and the tire deformation itself is small. The vibration of the two is close to that of the passive suspension. Since the tire deformation itself is small, as long as VBGH is better than ABGH, it is meaningful. The simulation of the VBGH-ABGH algorithm proposed later also illustrates this point.

[0121] 2.2. VBGH-ABGH algorithm proposed in this paper

[0122] First, define and determine the crossover frequency Ω for operational stability, according to Figure 3As can be seen from the parameters in Table 1, the frequency is around 7Hz. In the low-frequency region (0.2-2Hz) and before the crossover frequency Ω, the performance of the ABGH algorithm is close to that of the minimum damping passive suspension, but the performance of the VBGH algorithm is worse than that of the high-damping passive suspension; in the mid-frequency region (2-7Hz), the performance of both algorithms is close to that of the small-damping passive suspension, but in the mid-frequency band, the effect of the increase in wheel dynamic load on the handling performance can be ignored because the resonant frequency points of the sprung and unsprung masses will not be set in this range. When the dominant frequency of the road excitation is greater than the crossover frequency Ω, VBGH is better than ABGH. Since the vertical axis uses a logarithmic coordinate, Figure 3 The local enlarged picture can be seen clearly.

[0123] Based on these findings, the present invention proposes a hybrid VBGH-ABGH algorithm for stability. This algorithm online identifies the dominant frequency of the road surface and adaptively calculates the stability-oriented crossover frequency Ω. By comparing the two, it switches between VBGH and ABGH, leveraging the performance advantages of both in different excitation frequency bands. Specifically, the hybrid VBGH-ABGH algorithm switches to ABGH when the dominant frequency of the road surface is in the low-frequency range and to VBGH when the dominant frequency is in the high-frequency range.

[0124] The hybrid VBGH-ABGH algorithm expression is as follows:

[0125]

[0126] Where Ω is the crossover frequency for operational stability, f excitation is the dominant frequency of the road excitation.

[0127] Different from the SH-ADD algorithm, the present invention switches to the FFT method to identify the dominant frequency f of the road excitation. excitation The specific method is as follows:

[0128] 1) Using the vertical acceleration sensor signal installed at the wheel axis As the input signal of FFT.

[0129] 2) If Figure 4 As shown, the dominant frequency f of the road surface excitation using a sliding window excitation Dynamic identification, specifically:

[0130] First, divide the vertical acceleration sensor signal processed by FFT by the number of points N, then remove the periodicity, and finally take the modulus of the signal. The largest modulus is the dominant frequency f excitation After a lot of simulation tests, we selected FFT parameter N=1024 and sampling frequency 1kHz. The simulation input signal is:

[0131]

[0132] Among them, w(t) is a white noise random signal, and the simulation verification results are as follows: Figure 5 shown.

[0133] from Figure 5 It can be seen that the method of using sliding window FFT proposed in the present invention can better identify the active frequency signal of the road surface.

[0134] Frequency response simulation of hybrid VBGH-ABGH algorithm is shown in Figure 6 .

[0135] from Figure 6 It can be seen that the hybrid VBGH-ABGH algorithm has the best performance in both low-frequency and high-frequency regions. That is, before the crossover frequency point, it is almost better than the ABGH algorithm in the low-frequency region and almost close to the VBGH algorithm in the high-frequency region.

[0136] In order to further verify the hybrid VBGH-ABGH algorithm proposed in this embodiment, two typical frequency harmonic excitations and random excitations are simulated in the time domain.

[0137] First, two typical frequency simple harmonic sinusoidal signals Asin(w i t) as the road excitation, where w1 = 1.5 Hz (sprung mass resonance frequency) and w2 = 10 Hz (unsprung mass resonance frequency). The tire deformation response is as follows Figure 7 shown.

[0138] (A=0.05)

[0139] from Figure 7 As can be seen, when the road excitation frequency is 1.5Hz, the performance of the VBGH algorithm deteriorates compared to the nominal passive suspension, while the ABGH algorithm demonstrates excellent performance. Specifically, in the low-frequency region, ABGH outperforms both VBGH and the passive suspension. Notably, as expected, the hybrid VBGH-ABGH algorithm performs almost as well as ABGH. The proposed hybrid VBGH-ABGH algorithm improves stability performance by 38% and 31% compared to the passive suspension, as measured by RMS and MaxAE, respectively.

[0140] from Figure 7 As can be seen from the figure, under 10Hz excitation, VBGH shows the best performance, while the hybrid VBGH-ABGH algorithm, as expected, shows the same performance as VBGH. The hybrid VBGH-ABGH algorithm even improves the RMS value by 5.8% compared to the ABGH algorithm. This may not seem significant at first glance, but the RMS value of the tire deformation increased by 2.6mm relative to the unsprung mass resonance frequency point is still a significant improvement.

[0141] For the simulation of random conditions, ISO-8608D level random road input is used, the vehicle speed is 60km / h, the time domain response of tire deformation, RMS value and maximum error value MaxAE are as follows Figure 8 As shown in Table 2.

[0142] Table 2 ISO-8608 D-grade road input tire deformation

[0143]

[0144] from Figure 8 As can be seen from the statistical data in Table 2, the performance of the hybrid VBGH-ABGH algorithm has been significantly improved, outperforming both the VBGH and ABGH algorithms. The hybrid VBGH-ABGH algorithm improves the RMS value of the passive suspension by 29.4%.

[0145] Example 2: Stability-oriented crossover frequency adaptive calibration method

[0146] Maintain the tire stiffness K of each wheel w In the range of the mass on each wheel spring, the mass on the spring m s The value increases from the minimum value to the maximum value with the set step length, and the value of the mass on each wheel spring at each incremental order is obtained: m s =[m s,1 m s,2 …m s,N ]; where N is the increment order. Similarly, the tire stiffness of each wheel is increased from the minimum value to the maximum value in a set step size within the value range to obtain the value of each tire stiffness at each increment order:

[0147] K w =[K w,1 K w,2 …K w,N ]

[0148] For wheel i, the mass on the sprung wheel is m s,n , tire stiffness is K w,h Under the condition of VBGH control algorithm and ABGH algorithm, the frequency response analysis is carried out to obtain the crossover frequency Ω for stability. n,h Traverse n = 1, ... N, h = 1, ..., N, and obtain the stability-oriented cross-frequency for each combination of sprung mass and tire stiffness, and obtain the N × N-dimensional stability-oriented cross-frequency matrix:

[0149]

[0150] The input signal k of the present invention w and m sNo additional sensors are required, and only existing equipment on commercial vehicles can be used. w It can be calculated from the tire pressure sensor according to the following formula:

[0151]

[0152] Where P is tire pressure, unit is Pa, W is tire width, D is tire diameter, unit is m. s It can be obtained by solving the dynamic equation when the vehicle is stationary. m s It is a function of the deformation of the suspension when it is in static equilibrium, and the formula is as follows:

[0153]

[0154] k b is the suspension stiffness; Δs is the static balance and is the suspension deformation, which can be obtained through the dynamic displacement sensor or angle sensor installed on the intelligent suspension vehicle. Based on the above offline calculation, based on the current (k w ,m s ) data pairs, the present invention proposes an adaptive update mechanism to update the crossover frequency in the algorithm for comfort and stability. w ,m s ) will not change significantly during vehicle driving, so it is not necessary to update the crossover frequency in each control cycle. The present invention only updates the crossover frequency when the vehicle is started, and it will not be updated again until the next time the driver starts the vehicle. When switching between the ABGH control algorithm and the VBGH control algorithm, the crossover frequency is updated according to the vehicle's current (k w ,m s ) data, find (k w ,m s ) data, and compare the dominant frequency of the road surface excitation in the frequency domain to complete the switching between the two algorithms. The principle diagram of the adaptive cross frequency update mechanism is shown in Figure 9 .

[0155] Example 3: Comfort-oriented SH-ADD algorithm based on comfort cross-frequency.

[0156] When determining the crossover frequency of the hybrid SH-ADD algorithm, the present invention follows the method of Example 2, and for the i-th wheel, the sprung mass is m s,n , tire stiffness is K w,h Under the condition of SH algorithm and ADD algorithm, the frequency response analysis is carried out to obtain the comfort-oriented crossover frequency α n,hTraverse n = 1, ... N, h = 1, ..., N, and obtain the comfort-oriented cross-frequency for each combination of sprung mass and tire stiffness, and obtain the N × N dimensional comfort-oriented cross-frequency matrix:

[0157]

[0158] Similar to Example 2, the present invention only updates the crossover frequency when the vehicle is started, and it will not be updated until the next time the driver starts the vehicle. When switching between the SH control algorithm and the ADD control algorithm, the crossover frequency is updated according to the vehicle's current (k w ,m s ) data, find (k w ,m s ) data, and compare the dominant frequency of the road surface excitation in the frequency domain to complete the switching of the two algorithms.

[0159] Figure 10 2-D map for crossover frequency calibration for comfort and stability. Figure 10 As can be seen from the figure, the comfort-oriented crossover frequency increases with increasing tire stiffness and decreases with increasing sprung mass; it depends on both variables, with changes in sprung mass having a more significant impact than changes in tire stiffness. Simulation observations are consistent with theoretical expectations. The comfort metric is sprung mass acceleration, which is primarily influenced by sprung mass. The stability-oriented crossover frequency is proportional to both sprung mass and tire stiffness, and depends on both variables. The influence of tire stiffness is more significant than that of sprung mass. Notably, this relationship is the opposite of that for the comfort-oriented crossover frequency.

[0160] Example 4: State variable estimation method

[0161] Estimating state variables is a key issue in the engineering implementation of semi-active suspension systems. The SH, ADD, and VBGH-ABG algorithms all require feedback on the absolute and relative velocities of the sprung and unsprung masses, which are difficult to measure on a vehicle. To address this issue, this paper proposes a real-time state estimation method based on FFT frequency-domain integration. This method requires only two accelerometers, one mounted on the sprung and one on the unsprung masses. The detailed implementation method and simulation validation demonstrate its effectiveness are presented below.

[0162] The implementation method based on FFT frequency domain integration is as follows:

[0163] The acceleration signal a(t) of a certain frequency ω component is:

[0164] a(t)=Ae jωt

[0165] A is the amplitude, the initial value of velocity is zero, and the velocity signal obtained by integration is:

[0166]

[0167] Where v(t) is the Fourier velocity component with frequency ω; V is the velocity amplitude, V = A / jω. When the initial velocity and displacement are zero, the displacement x(t) can be obtained by integrating the following formula:

[0168]

[0169] Where x(t) is the Fourier component of the displacement with frequency ω; X is the coefficient corresponding to x(t), X = -A / ω 2 .

[0170] After the Fourier transform of all different frequency components is completed, the time-domain velocity and displacement signals can be obtained through the inverse Fourier transform. Considering the measurement accuracy of the accelerometer in the low-frequency region, in actual projects, the low-frequency response components can be set to 0 according to actual conditions. For example, set the frequency components below 1 Hz to zero.

[0171] In the control algorithm proposed in the present invention, only the speed of the vibration response needs to be calculated in real time. Based on the above theory, the implementation process of the online real-time estimation algorithm of the present invention is shown in FIG. Figure 11 The implementation process of the online state estimation algorithm is as follows:

[0172] (1) When the vehicle is started, in order to improve timeliness, the algorithm runs on a buffer window with a length of N, a number of sliding times k (k = 0, 1, 2, ...), and a sliding distance of ΔN. The sample is taken once every 1ms, and the buffer window is 1024 (N) sampling points. The acceleration signal is then (According to the different sensor positions, the acceleration signal is divided into sprung mass acceleration signal and unsprung mass acceleration signal But the processing is the same) is stored in the buffer, where n = 0, 1, ..., N-1;

[0173] (2) After completing a window period of sampling, low-pass filtering is performed, and the cutoff frequency is selected as 50 Hz.

[0174] (3) The time domain acceleration signal sampled during the window period Perform FFT transformation to obtain the time domain acceleration signal A k [ω];

[0175] (4) After FFT transformation, the frequency domain signal A k [ω] The frequency components below 1 Hz are set to zero amplitude;

[0176] (5) Then, the frequency domain integration theory is applied to the signal obtained in step (4) to perform frequency domain integration processing to obtain the frequency domain velocity signal:

[0177]

[0178] (6) The frequency domain signal of the velocity is obtained by IFFT to obtain the time domain signal of the velocity:

[0179]

[0180] (7) Since each window is processed independently, the integration process does not consider the cumulative nature of the velocity signal between windows, that is, the initial value of each integration is zero, which leads to discontinuities at the boundaries of consecutive windows. In order to eliminate this error, an offset correction mechanism is applied:

[0181] When k = 0, the data in the window are integrated with zero initial value and no correction is required;

[0182] When k ≥ 1, the initial value of this integration is set to the integration value at ΔN (a total of (k-1) ΔN data) in the previous window. This is equivalent to adding this value as an offset increment to the current integration data. The corrected speed can be obtained by the following formula:

[0183]

[0184] This ensures that the first point in the new window is aligned with the delta point of the previous window, satisfying:

[0185]

[0186] This offset correction method maintains signal continuity between consecutive sliding windows and is crucial for achieving accurate, real-time state variable estimation in semi-active suspension control algorithms.

[0187] (8) After sampling ΔN increments, the window is slid once and the process from the second step (2) to the eighth step (8) is repeated, thereby realizing the online estimation process of the speed state. The vehicle simulation parameters and the algorithm parameters obtained after a large number of optimizations are shown in Table 3.

[0188] Table 3 Simulation parameters based on FFT online calculation of state variables

[0189]

[0190] Among them, z road Represents the road surface grade; v longtitudinal is the vehicle speed; t initialization is the initialization time; f accelerometer is the acceleration sampling frequency; f estimationis the estimated frequency of velocity, that is, the frequency at which the algorithm obtains velocity. N is the number of sampling data points in the acceleration window, and ΔN is the increment of the number of sampling points in the sliding window.

[0191] The simulation diagram of the proposed online velocity estimation method is shown in Figure 12.

[0192] from Figure 12 Compared with theoretical data, the proposed algorithm can accurately estimate the sprung mass velocity (the unsprung mass velocity can also be obtained), meeting the accuracy requirements for algorithm engineering implementation, with a maximum MaxAE of less than 0.005 m / s. This method eliminates obstacles to the engineering implementation of the proposed semi-active control algorithm.

[0193] Example 5: A semi-active suspension adaptive control method

[0194] 5.1. Algorithm proposed in this invention

[0195] Based on the above invention points, the present invention proposes an adaptive method for semi-active suspension. The idea of this method is:

[0196] (1) Combine the above hybrid SH-ADD and hybrid VBGH-ABGH, and use an upper-level controller to switch between the above two according to the collected typical working condition vehicle information and driver control signals, so as to decide in real time whether to focus on comfort or handling stability. The principle diagram of the control method proposed by the present invention is shown in Figure 13 .

[0197] (2) Figure 13 As shown, the switching logic proposed by the present invention calculates lateral acceleration online based on vehicle speed and steering angle signals. Excessive lateral acceleration can lead to unbalanced wheel loads, which can cause the vehicle to skid. When the lateral acceleration exceeds a set threshold, the upper-level controller switches to stability as the control objective; otherwise, it switches to comfort as the control objective. Specifically:

[0198]

[0199] Among them, c SH-ADD represents the damping determined by the mixed SH-ADD, c VBGH-ABGH Representative damping is determined by the mixture VBGH-ABGH. is the calculated lateral acceleration, α treshold Represents the lateral acceleration threshold.

[0200] (3) The acquisition of lateral acceleration can be achieved using an IMU sensor. Considering the cost and lag of direct measurement, the present invention proposes a method for calculating lateral acceleration based on vehicle speed and steering angle. The calculation formula is as follows:

[0201]

[0202] Where L is the wheelbase; K us is the understeer coefficient, v is the vehicle speed, and δ is the steering wheel angle. The values of these parameters can be determined based on the specific vehicle. The real-time calculated value can be filtered through a high-pass filter to eliminate DC components and low-frequency noise. Figure 14 The calculated value of lateral acceleration is compared with the CARSIM simulation.

[0203] 5.2. Simulation Verification of the Algorithm Proposed in This Invention

[0204] The proposed algorithm has been verified to have a good effect by using the joint simulation method of CarSim and Simulink. The joint simulation structure diagram is shown in Figure 15 .

[0205] Scenario 1: Sudden lane change to avoid obstacles. The lane change test scenario consists of Figure 16 given.

[0206] The simulation results are shown in Figure 17 .

[0207] from Figure 17 It can be seen that the strategy transition during lane change (a), track tracking effect (b) and tire dynamic load (c) (dynamic load is minimized when switching to stability mode) prove the effectiveness of the strategy proposed in scenario 1.

[0208] Scenario 2: High-speed curved track, the test scenario consists of Figure 18 given.

[0209] The simulation results are shown in Figure 19 、 Figure 20 .

[0210] from Figure 20 Statistical calculations show that the passive suspension wheel hop occurs 12 times, while the control method of the present invention reduces it to 2 times. According to statistics, the root mean square value of vertical acceleration is reduced by 32% compared with the passive suspension. Figure 19 The vehicle status diagram of two instants of simulation was intercepted. Figure 18 High-speed curved track (b) - At the sharp turn of about 150m, Figure 19 (a) It can be seen that the passive suspension vehicle has slipped off the track, while the vehicle equipped with the semi-active control strategy of the present invention has better operational stability than the passive suspension vehicle. Figure 19 (b) is a screenshot from the finish line of the race. The blue vehicle, equipped with the control strategy of the present invention, reaches the finish line ahead of the red vehicle. This demonstrates the effectiveness of the proposed strategy in Scenario 2. Scenarios 1 and 2 represent typical driving conditions and fully demonstrate the effectiveness of the control method proposed in this invention.

[0211] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A vehicle semi-active suspension adaptive control method, characterized in that: include: In the case of operational stability control, when the dominant frequency of the road excitation is f excitation When the dominant frequency of the road excitation is greater than the set crossover frequency Ω, the VBGH control algorithm is used to control the vehicle.

2. The vehicle semi-active suspension adaptive control method according to claim 1, characterized in that: The calculation method of the set crossover frequency Ω includes: Maintain the tire stiffness K of each wheel w In the range of the mass on each wheel spring, the mass on the spring m s The value increases from the minimum value to the maximum value with the set step length, and the value of the mass on each wheel spring at each incremental order is obtained: m s =[m s,1 m s,2 … m s,N ]; where N is the increment order; Increase the tire stiffness of each wheel within the value range from the minimum value to the maximum value with a set step size, and obtain the value of the tire stiffness of each wheel at each incremental order: K w =[K w,1 K w,2 … K w,N ]; For wheel i, the mass on the sprung wheel is m s , tire stiffness k w Under the condition of VBGH control algorithm and ABGH algorithm, the frequency response analysis is carried out to obtain the crossover frequency Ω for stability. n,h Traverse n = 1, ..., N, h = 1, ..., N, and obtain the stability-oriented cross-frequency for each combination of sprung mass and tire stiffness, and obtain the N × N-dimensional stability-oriented cross-frequency matrix: When switching between the ABGH and VBGH control algorithms, the crossover frequency corresponding to the vehicle's current sprung mass and tire stiffness data is found in the stability-oriented crossover frequency matrix. This frequency is then compared with the dominant frequency of the road excitation to complete the switch between the two algorithms.

3. The vehicle semi-active suspension adaptive control method according to claim 2, characterized in that: Tire stiffness k w Calculation based on tire pressure sensor: Where P is tire pressure, W is tire width, and D is tire diameter; sprung mass m s Calculated by the following formula: Where g represents the acceleration due to gravity, k b is the suspension stiffness; Δs is the suspension deformation in static equilibrium, which is obtained through the dynamic displacement sensor or angle sensor installed on the intelligent suspension vehicle.

4. A vehicle semi-active suspension adaptive control method according to claim 1, 2 or 3, characterized in that: Road surface excitation dominant frequency f excitation Obtained using the following method: Collect vertical acceleration sensor signals at the wheel axis After performing FFT, first divide it by the number of points N, then remove the periodicity, and finally take the modulus of the signal. The largest modulus is the dominant frequency f of the road excitation. excitation .

5. The method for adaptive control of a vehicle semi-active suspension according to claim 4, characterized in that: The crossover frequency is updated every time the vehicle is started.

6. A vehicle semi-active suspension adaptive control method, characterized in that: In the control of operational comfort, when the dominant frequency of road excitation f excitation When the road surface excitation dominant frequency is greater than the set crossover frequency α, the vehicle is controlled using the ADD control algorithm. The calculation method of the crossover frequency α includes: Maintain the tire stiffness K of each wheel w In the range of the mass on each wheel spring, the mass on the spring m s The value increases from the minimum value to the maximum value with the set step length, and the value of the mass on each wheel spring at each incremental order is obtained: m s =[m s,1 m s,2 … m s,N ]; where N is the increment order; Increase the tire stiffness of each wheel within the value range from the minimum value to the maximum value with a set step size, and obtain the value of the tire stiffness of each wheel at each incremental order: K w =[K w,1 K w,2 … K w,N ]; For wheel i, the mass on the sprung wheel is m s , tire stiffness k w Under the condition of SH algorithm and ADD algorithm, the frequency response analysis is carried out to obtain the comfort-oriented crossover frequency α n,h Traverse n = 1, ..., N, h = 1, ..., N, and obtain the comfort-oriented cross-frequency for each combination of sprung mass and tire stiffness, and obtain an N × N-dimensional comfort-oriented cross-frequency matrix: Based on the vehicle's current sprung mass and tire stiffness data, the crossover frequency corresponding to the current sprung mass and tire stiffness data is found in the comfort-oriented crossover frequency matrix, and the SH algorithm and ADD algorithm are switched based on this crossover frequency.

7. A vehicle semi-active suspension adaptive control method according to claim 1 or 6, characterized in that: The state variable estimation methods required for vehicle control include: (1) When the vehicle is started, a sliding window is used to sample the acceleration signals of the sprung mass and the unsprung mass. The interval between adjacent sliding windows is ΔN sampling points. The width of each sliding window is N, and the sequence number of the sliding window is k. The sampling is performed once every 1 ms, and the sliding window width is N = 1024 sampling points. The acceleration signal Stored in a buffer, where n = 0, 1, ..., N-1; (2) After completing a sliding window sampling, the acceleration signal is low-pass filtered; (3) Acceleration signal after low-pass filtering Perform FFT transformation to obtain the time domain acceleration signal A k [ω]; where ω represents frequency; A k [ω] represents the acceleration signal corresponding to the kth sliding window; (4) After FFT transformation, the frequency domain signal A k [ω] The frequency components below 1 Hz are set to zero amplitude; (5) Then, the frequency domain integration theory is applied to the signal obtained in step (4) to perform frequency domain integration processing to obtain the frequency domain velocity signal: (6) The frequency domain signal of the velocity is obtained by IFFT to obtain the time domain signal of the velocity: (7) When k ≥ 1, during the frequency domain integration process, the initial value of this integration is set to the integral value at ΔN obtained in the previous sliding window, which is equivalent to adding this value as an offset increment to the current integration data. The corrected velocity is obtained by the following formula: (8) After every ΔN sampling points, the sliding window moves once, and the process from the second step (2) to the eighth step (8) is repeated, thereby realizing the online estimation process of the speed state.

8. A vehicle semi-active suspension adaptive control method, characterized in that: The upper controller switches between the hybrid SH-ADD control algorithm and the hybrid VBGH-ABGH control algorithm according to the lateral acceleration to achieve vehicle control. That is, when the lateral acceleration is less than the set threshold, the hybrid SH-ADD control algorithm is used; otherwise, the hybrid VBGH-ABGH control algorithm is used. Among them, the hybrid VBGH-ABGH control algorithm is used, when the dominant frequency of the road excitation f excitation When the dominant frequency of the road excitation is greater than the set crossover frequency Ω, the VBGH control algorithm is used to control the vehicle. In the hybrid SH-ADD control algorithm, when the dominant frequency of the road excitation f excitation When the dominant frequency of the road excitation is greater than the set crossover frequency α, the ADD control algorithm is used to control the vehicle.

9. The vehicle semi-active suspension adaptive control method according to claim 8, characterized in that: The lateral acceleration is calculated as follows: Wherein, L is the vehicle wheelbase; K us is the understeer coefficient, v is the vehicle speed, and δ is the steering wheel angle.

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