GB2618362A - Method of designing a vehicle suspension system - Google Patents

Abstract

Method of designing a vehicle suspension by computer simulation, by determining a time-varying first and second suspension response outputs in response to a time-varying first and second suspension disturbance inputs, based on a simulated suspension system having a non-linear damping function, the first and second suspension disturbance inputs falling within first and second statistical distributions different from the first statistical distribution; and generating spectra (may be Fourier transform) of each of the first and second suspension response outputs, each of the spectra indicating suspension force transmissibility as a function of frequency. The difference in statistical distribution may be in amplitude, which the second disturbance input may be a scaled version of the first, high enough to cause the non-linear damping function to enter the non-linear region. A third response output in response to a third suspension disturbance input may be simulated. Graphical output of the spectra may be generated, and the suspension design parameter modified (passive or active damping coefficient control parameter).

Description

METHOD OF DESIGNING A VEHICLE SUSPENSION SYSTEM

TECHNICAL FIELD

The present disclosure relates to a method of designing a vehicle suspension system. In particular, but not exclusively it relates to a method of design, to a vehicle suspension system having suspension parameters designed according to the method, and to a vehicle comprising the vehicle suspension system.

BACKGROUND

Vehicle suspension systems typically comprise springs and dampers. Automotive dampers may have a force-velocity relationship that is assumed to be linear at the design stage, but in reality the relationship may be nonlinear.

Test methods exist for ensuring that a suspension tune achieves a desired attribute. A range of road frequencies may be tested by experiments and/or simulation. It would be useful to identify the full range of possible operating conditions of the suspension at the design stage.

SUMMARY OF THE INVENTION

It is an aim of the present invention to address one or more of the disadvantages associated with the prior art.

Aspects and embodiments of the invention provide a method of designing a vehicle suspension system, a vehicle suspension system, and a vehicle, as claimed in the appended claims According to an aspect of the present invention there is provided a method of designing a vehicle suspension system, the method comprising: determining, via computer-implemented simulation, a time-varying first suspension response output, in response to a time-varying first suspension disturbance input, based on a simulated suspension system having a nonlinear damping function, the first suspension disturbance input comprising disturbances falling within a first statistical distribution; determining, via computer-implemented simulation, a time-varying second suspension response output, in response to a time-varying second suspension disturbance input, based on the same simulated suspension system having the same nonlinear damping function, the second suspension disturbance input comprising disturbances falling within a second statistical distribution different from the first statistical distribution; and generating spectra of each of the first and second suspension response outputs, each of the spectra indicating suspension force transmissibility as a function of frequency.

An advantage is that the method enables selection of improved suspension design parameters. This is because the spectra inform the designer of how the peak suspension force transmissibility is affected by road characteristics such as roughness. Based on the results, the designer can try different appropriate suspension design parameters until the relationship is suitably smooth and predictable, and meets NVH (noise, vibration, harshness) and/or vehicle controllability attribute targets. A vehicle suspension system designed according to this method has more robust behaviour and a lower likelihood of unexpected oscillations in edge case driving situations.

In some examples, the second statistical distribution comprises a different statistical amplitude distribution than the first statistical distribution. In some examples, the second suspension disturbance input is an amplitude-scaled version of the first suspension disturbance input. In some examples, the first and second suspension response outputs are determined based on a same vehicle speed. An advantage of scaling the input signal while keeping various other factors constant is that it provides a representative, controlled input.

In some examples, at least one of the first and second statistical amplitude distributions comprises disturbances of a high enough amplitude to cause the nonlinear damping function to enter a nonlinear region. An advantage is more accurate modelling at high load conditions.

In some examples, the disturbances of the first and/or second suspension disturbance input are substantially randomly-generated, and configured to fall within the first statistical distribution.

In some examples, the disturbances of the first and/or second suspension disturbance input are at a plurality of different frequencies.

In some examples, generating the spectra comprises performing a Fourier transform of each of the first and second suspension response outputs.

In some examples, the method comprises: determining, via computer-implemented simulation, one or more further time-varying suspension response outputs, each in response to a different time-varying suspension disturbance input, each based on the same simulated suspension system having the same nonlinear damping function, each different suspension disturbance input comprising disturbances falling within a different statistical distribution than each other and than the first and second statistical distributions; wherein generating the spectra comprises generating further spectra of each of the further suspension response outputs, each of the further spectra indicating suspension force transmissibility as a function of frequency.

In some examples, the method comprises causing graphical output of the spectra on a common set of axes.

In some examples, the method comprises: comparing force transmissibility peaks of the different spectra; modifying at least one suspension design parameter based on the comparison; and outputting to a file the results of the method.

In some examples, the method comprises: plotting a line approximately intersecting the maximum force transmissibility peaks of the spectra; wherein modifying the at least one suspension design parameter is based on the line intersecting the maximum force transmissibility peaks.

In some examples, the suspension design parameter comprises a passive damping coefficient or an active damping coefficient control parameter.

According to a further aspect of the present invention there is provided a vehicle suspension system having suspension parameters designed according to the method.

According to a further aspect of the present invention there is provided a vehicle comprising the vehicle suspension system.

According to a further aspect of the present invention there is provided a control system for designing a vehicle suspension system, the control system comprising one or more controllers, the control system configured to cause, at least in part, execution of any one or more of the methods described herein.

The one or more controllers may collectively comprise: at least one electronic processor having an electrical input for receiving one or more input signals from an input device; and at least one memory device electrically coupled to the at least one electronic processor and having instructions stored therein; and wherein the at least one electronic processor is configured to access the at least one memory device and execute the instructions thereon so as to cause, at least in part, execution of any one or more of the methods described herein.

According to a further aspect of the invention there is provided computer software that, when executed, is arranged to perform any one or more of the methods described herein. According to a further aspect of the invention there is provided a non-transitory computer readable medium comprising computer readable instructions that, when executed by one or more electronic processors, causes the one or more electronic processors to carry out any one or more of the methods described herein.

Within the scope of this application it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination that falls within the scope of the appended claims. That is, all embodiments and/or features of any embodiment can be combined in any way and/or combination that falls within the scope of the appended claims, unless such features are incompatible. The applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to amend any originally filed claim to depend from and/or incorporate any feature of any other claim although not originally claimed in that manner.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which: FIG. 1 illustrates an example of a vehicle; FIG. 2 illustrates an example of a control system; FIG. 3 illustrates an example of a non-transitory computer-readable storage medium; FIG. 4 illustrates a single degree of freedom suspension system subjected to road input; FIG. 5 illustrates a damper force-velocity curve; FIG. 6 illustrates how a road factor affects a relationship between the maximum resonant suspension response and frequency; FIG. 7 illustrates road disturbances of an example Factor 1 road as a function of time for a given vehicle speed; FIG. 8 illustrates the road disturbances of FIG. 7, amplified by approximately 4.5 times; FIG. 9 illustrates an example method; FIG. 10 illustrates simulation results for a front suspension; and FIG. 11 illustrates simulation results for a rear suspension.

DETAILED DESCRIPTION

FIG. 1 illustrates an example of a vehicle 1 in which embodiments of the invention can be implemented. In some, but not necessarily all examples, the vehicle 1 is a passenger vehicle, also referred to as a passenger car or as an automobile. In other examples, embodiments of the invention can be implemented for other applications, such as commercial vehicles.

FIG. 1 is a front perspective view and illustrates a longitudinal x-axis between the front and rear of the vehicle 1 representing a centreline, an orthogonal lateral y-axis between left and right lateral sides of the vehicle 1, and a vertical z-axis. A forward/fore direction typically faced by a driver's seat is in the negative x-direction; rearward/aft is +x. A rightward direction as seen from the driver's seat is in the positive y-direction; leftward is -y. These are a first lateral direction and a second lateral direction.

FIG. 2 illustrates an example control system 200 configured to implement one or more aspects of the invention. The control system 200 may comprise a personal computer, laptop or any other suitable computing device. The control system 200 of FIG. 2 comprises a controller 201.

The controller 201 of FIG. 2 includes at least one processor 204; and at least one memory device 206 electrically coupled to the electronic processor 204 and having instructions (e.g. a computer program 208) stored therein, the at least one memory device 206 and the instructions configured to, with the at least one processor 204, enable any one or more of the methods described herein to be performed. The controller 201 may have an interface 202 comprising an electrical input/output I/O 210, 212, or an electrical input 210, or an electrical output 212, for receiving information and interacting with external components such as an input device 214 (e.g., keyboard, touchscreen or any other human-machine interface) and an output device 216 (e.g., electronic display).

FIG. 3 illustrates a non-transitory computer-readable storage medium 300 comprising the instructions 208 (computer software).

FIG. 4 illustrates a mass-spring-damper diagram of a vehicle suspension system 400 for a vehicle wheel 2 of a vehicle 1. The vehicle 1 is moving at a constant velocity v over sinusoidal terrain having a terrain height amplitude Y and wavelength L. A quarter vehicle model is illustrated in FIG. 4, showing only the suspension for a single quarter of the vehicle 1. The vehicle suspension system 400 can further comprise suspensions for each other quarter (corner) of the vehicle 1. To simplify explanation, the term 'vehicle suspension system' refers to the suspension shown in FIG. 4 but is not limited to only one suspension for one vehicle wheel 2.

FIG. 4 illustrates a sprung mass in. The sprung mass represents the portion of the total vehicle mass which is borne by the illustrated vehicle suspension system 400.

The vehicle suspension system 400 of FIG. 4 comprises a spring 402 and a damper 404 in parallel to each other. Either one or both of them may be passive, semi-active, or active.

Spring force is proportional to spring displacement and damper force is proportional to damper velocity. The shape of the curve describing this relationship is controlled by the designer by choosing the appropriate suspension parameters for the spring 402 and the damper 404. The suspension parameters include spring stiffness k and damping coefficient c". These may be variables or constants depending on the specific implementation.

In order to determine if a vehicle suspension system will provide an acceptable level of vertical control of a vehicle 1, the designer runs dynamic tests using representative inputs. The representative inputs represent the types of road that the vehicle 1 is configured to drive on, varying from smooth roads to rough road surfaces.

From this test, the designer can evaluate the ratio of output to input forces through the frequency range they expect the vehicle 1 to see. This ratio is referred to as the force transmissibility (or just transmissibility). The input force is the force input to the vehicle wheel, measured experimentally or virtually at the hub or measured virtually at the bottom of the tyres. The output force is the force transmitted to the sprung mass of the vehicle 1, measured for example at a strut tower. The output force may comprise the summed force from the spring and damper. A well-tuned vehicle suspension system will ensure that the output force is lower than the input force.

If the transmissibility is acceptable through the frequency range tested, the designer can infer with reasonable certainty that any cyclic events will be well controlled.

From classical vibration theory, the following relationship between instant road input yRoad and the peak amplitude of the road undulations YRoad can be derived from FIG. 4, assuming that the vehicle suspension system 400 behaves with a single degree of freedom: YRoad(X) = 'Road Sin(27tx IL) The system of FIG. 4 is governed by the following equation: At) = kx + c± + ml. In this relationship, f (t) is force with respect to time. k is the stiffness of the system, such as 'wheel rate' -the effective stiffness of the vehicle suspension system 400 measured at the vehicle wheel centre. x is taken as the displacement between the sprung mass in and the vehicle wheel. c is the linear coefficient of damping. X is the velocity of the un-sprung mass relative to the wheel mass. X is the acceleration of the sprung mass relative to the wheel.

There are three potential solutions to the above equation as it is a linear homogeneous differential equation. These solutions depend on the roots of the characteristic equation. This disclosure will not cover the detail of the solutions to this differential equation but, they are known as the underdamped solution, critically damped solution, and overdamped solution which are based on the roots of the characteristic equation. In the derivation of these solutions, an important quantity becomes apparent -the natural frequency wn: con = "F -mic. This is the frequency that if the system is excited at there would result a peak in force, displacement, velocity and acceleration.

As the number of degrees of freedom increase in the vehicle suspension system 400 the number of natural frequencies also increase by that same number. Some of these modes have discrete names in vehicle dynamics, for example 'primary ride' (typically 0.75 to 1.2 Hz), secondary ride, bounce, hop etc. The resonant frequency of most interest is primary ride which, usually falls between 0.75 Hz and 1.2 Hz and is the mode associated with the stiffness of the springs. Note that the rear spring rate and therefore rear primary ride frequency may be about 10% higher than the front, plus or minus 5%.

Using the above equations, it is possible to develop a relationship between input force at a particular frequency and output force at the same frequency. This relationship between output and input force at particular frequencies is the force transmissibility. A force transmissibility plot for single degree of freedom system is shown in FIG. 6. FIG. 6 shows a plurality of transmissibility plots 600, each for a different road roughness (labelled IRF', road factor).

Given this background from classical vibration theory, if the designer wishes to control the output force transferred into the sprung mass, they must ensure that at the frequencies the vehicle 1 is known to resonate at must have an appropriate level of damping 5. This will ensure that the output force is not magnified to a point where the output force will cause damage to the vehicle 1.

If the damping coefficient is increased, it becomes apparent that less damping or, a smaller percentage of critical damping results in an increased vertical suspension load magnification factor (transmissibility). Given this, it is possible to specify a maximum vertical suspension 8 load magnification factor and control it by increasing or decreasing the amount of damping in the system by tuning the damping coefficient.

The present disclosure addresses a problem relating to nonlinear behaviour of the damper.

An example damper curve 500 is shown in FIG. 5 with damper force Fd on the y axis as a function of damper extension/compression velocity v on the x-axis. The maximum plotted damper velocity may be in excess of ±0.75m/s (e.g., at least ±0.85m/s), representing the maximum envelope within which the damper is configured to operate.

For damper velocity in a first magnitude range, the damper force increases at a first rate. The first magnitude range is to both sides of zero velocity (=zero damper force). For damper velocity in a second magnitude range greater than the first magnitude range, the damper force increases at a second rate less than the first rate. This results in an S-curve shape rather than a linear shape. There is a knee point 502A, 502B as the relationship changes. The positive knee point 502A may occur at a damper velocity from the range 0.05m/s to 0.25m/s, depending on implementation. The negative knee point 502B may occur at a damper velocity from the range 0.05m/s to 0.25m/s, depending on implementation. The first magnitude range extends between endpoints 502A and 502B.

The shape may be asymmetric, the damper force being different depending on whether a value of damper velocity is positive or negative. The knee point 502A for a positive damper velocity may be different than the knee point 502B for a negative value of the same damper velocity.

The relationship of FIG. 5 is further complicated by variable damper valves and active systems. However, the relationship in any regime can still be characterised as Damping force c = c(X). This further implies f(t)= kx + c(X) + ml. The fact that c is no longer a linear coefficient but rather, a function of ± makes the differential equation unsolvable. It is also impossible to meaningfully linearise the damper curve 500 of FIG. 5 without compromising accuracy.

\F However, because con = 1 is still stiffness-driven, mass-driven and inertia-driven the resonant frequencies are still preserved. It can also be shown that (Odamped (peak transmissibility) does not diverge significantly (more than 2%) from con. Using this information, it is possible to experimentally determine the front and rear vertical suspension load magnification factors of the vehicle 1 for a particular section of the damper curve 500.

FIG. 6 illustrates a plurality of response curves 600 (transmissibility, y-axis) of a non-linear damper with progressive damping, each response curve 600 corresponding to a different input force magnitude. The x-axis is frequency. Other variables such as vehicle speed and suspension parameters are the same for each response curve 600. The successive response curves 600, starting from that with the lowest peak transmissibility, represent tests of a vehicle 1 travelling over an increasingly rough surface (increasing road factor 'RP), therefore the peak transmissibility to -damped increases. It is also possible to see the shift in Wdamped as the surface roughness (input force magnitude) increases. As the surface roughness increases, the shift may correspond to decrease in frequency at which the peak transmissibility occurs.

The graph of FIG. 6 also contains a line of maxima 602 that outlines the maximum response of the vehicle 1 across all the tests. The line passes through the peak transmissibility (maximum response at resonance) of a plurality of response curves 600 (each surface roughness tested). This line of maxima 602 can serve as a valuable enveloping technique, especially as dampers and control systems become more complex and their relationships less straightforward. The line of maxima may be straight or nearly straight, or in some cases could follow a curved path With reference to the line of maxima 602, it is possible to quickly determine the frequencies of primary interest (e.g., primary ride frequency for the front and rear vehicle wheels), and use that information in the search for the vertical suspension load magnification factor.

In order to understand the vehicle's response to loads that could be problematic, the dampers are excited, via computer-implemented simulation, to significantly beyond their knee points (e.g., beyond ±0.35m/s). The nonlinearity of the dampers is simulated. For example, the computer-implemented simulation may model the damper curve shape of FIG. 5 when solving the equations of motion across all the bodies in the modelled vehicle. In some examples, the dampers are excited to close to their maximum envelopes (e.g., beyond 0.5m/s or beyond 0.65m/s). A suitable simulation computer program is a mulfibody system simulation program such as Simpack(TM).

The simulated suspension disturbance input may comprise an input signal (virtual road) of terrain height y with respect to distance or time. The rate at which the input signal is applied to the simulated suspension depends on the simulated vehicle speed.

The input signal may comprise a plurality of positive and negative peaks relative to y=0, each peak representing a disturbance. The positive and negative peaks fall within a statistical amplitude distribution, in other words a statistical distribution of the amplitudes of the disturbances. The statistical amplitude distribution may optionally be approximately gaussian, or alternatively could follow a Poisson distribution or a log-normal distribution.

The input signal may comprise a pseudo-random signal. Pseudo-random signal generation captures the 'white noise' nature of road roughness, and ensures that the disturbances occur at a plurality of different frequencies. Alternatively, the input signal may comprise a sine sweep. Alternatively, the input signal may represent an actual road instead of being randomly generated.

The simulation is repeated a plurality of times, each for a different input signal. The input signal differs in that the disturbances fall within a different statistical distribution, representing different road roughness. The statistical amplitude distribution of the disturbances (the peaks) may be different between the simulations. This can be achieved by amplifying the input signal to widen or narrow the statistical amplitude distribution of the disturbances. The amplification can be controlled by a scaling factor, referred to herein as the 'road factor'. The road factor may be a multiplication factor.

The road factor controls an increase in road profile disturbance. For example, if there is one pothole in the input signal, a road factor of 1 means the pothole remains at 1 inch depth, however, a road factor 3 means that the pothole is now 3 inches in depth.

The advantage of scaling the input signal is that it provides a representative, controlled input that can be used to control the vertical input velocity of the vehicle wheel. This allows the designer to see the vertical suspension load magnification factor develop as a function of road input similar to the response curves 600 shown in FIG. 6.

It is noted that the international standards organisation (ISO) standard 8608 has created a standardised road roughness grading ranging from A -very good smooth concrete pavement, to F -extremely poor unfortified road. Each increasing grade has an increasing power spectral density. In the present disclosure, these power spectral densities can be used to filter a white noise signal and create a randomly generated road of a known roughness.

A grade A road has a power spectral density of from 1E-04 at a spatial frequency of 0.04 cycles/metre, to 1E-08 at a spatial frequency of 10 cycles/metre. A grade F road has a power spectral density of from 1E-01 at a spatial frequency of 0.04 cycles/metre, to 1E-05 at a spatial frequency of 10 cycles/metre. Therefore, the variation of power spectral density from best to worst may be three orders of magnitude, or two or four orders of magnitude.

In at least some examples, the statistical amplitude distribution for the present simulations is varied by at least two orders of magnitude, or by at least three orders of magnitude, between the best to worst simulated road type.

A lowest road factor that may be chosen for the present disclosure may represent an ISO grade A road. This is shown in the input signal of FIG. 7, which may be pseudo-random, wherein peak amplitudes of the signal are configured to not exceed ±Yi metres over a time series of t seconds (for a vehicle 1 travelling at a constant or known speed). Yl may be a value selected from the range 0.005m to 0.02m, depending on implementation. In FIG. 7, Y1 is 0.013m.

A highest road factor that may be chosen for the present disclosure may represent a worst road such as an ISO grade F road. A grade F road is represented in the input signal of FIG. 8, which is a scaled version of the same signal of FIG. 7. Peak amplitudes of the signal may be configured to not exceed ±Y2 metres over the same time series duration. Y2 may be a value selected from the range 0.03m to 0.06m, depending on implementation. In FIG. 8, Y2 is 0.057m, which is more than 4x higher than in FIG. 7.

The maximum tested road factor could be 4.5 times greater than the minimum tested road factor. In other examples, the maximum may have a 4.5 times greater amplitude than the minimum. In other examples, the maximum is 2 times greater or 3 times greater than the minimum. The tested road factor may further include intermediate values between the lowest and highest values described above.

The simulation may be repeated for each road factor, to generate a plurality of curves 600 as shown in FIG. 6. Then, the line of maxima 602 can be drawn through the peaks, to enable rapid determination of the frequencies of primary interest across the full range of road types.

By testing at least four different values of road factor, it is possible to draw the line of maxima with a high degree of precision to determine with reasonable accuracy the shift in peak transmissibility (shape of the line of maxima) for the full envelope of road types, including intermediate road types between the extremes.

Tests were conducted by the inventors in Simpack(TM), using six road factors and the suspension model of FIG. 4 within a detailed model. It was found that by the time the road factor had exceeded 4, it was clear how the damper would behave given a full range of road inputs. An extensive internal survey of a set of 20 vehicles was undertaken, encompassing several vehicle types and several model years. The survey found that road multiplication factors (road factors) ranging from 2 to 4.5 provided a meaningful and consistent walk from any vehicles' static weight condition to a proof load regime across the population. A proof load regime refers to a load regime that pushes components/vehicles close to their strength limits.

FIG. 9 is a flowchart illustrating a method 900 of designing a vehicle suspension system, in accordance with the methodology described above.

The method is especially useful for the design of vehicles which need to be off-road capable (e.g., all-wheel drive, high ride height) while also providing good on-road comfort and handling.

Blocks 902, 904 and 906 together relate to executing a first simulation. Blocks 912, 914 and 916 together relate to executing a second simulation. Blocks 922, 924 and 926 together relate to executing an optional nth simulation, where n>2.

Data block 902 comprises a time-varying first suspension disturbance input, comprising disturbances falling within a first statistical distribution. The first suspension disturbance input may be a first input signal of the type described earlier. The first input signal may be stored in at least one memory device.

The first input signal may be substantially randomly generated in the manner described earlier.

The first input signal may comprise disturbances (peaks) falling within a first statistical amplitude distribution. The first input signal may represent a best road such as a Grade A or B road. In an implementation, the first statistical amplitude distribution may be as shown in FIG. 7 and described.

In an implementation, the road factor used for generating the first input signal has a first multiplication value, so that the disturbances fall within the first statistical amplitude distribution.

Block 904 comprises determining, via computer-implemented simulation, a time-varying first suspension response output (first output signal), in response to the time-varying first suspension disturbance input (input signal), based on a simulated suspension system. The first output signal may represent the output force/input force ratio (transmissibility) described earlier.

The simulated suspension system models the spring and damper, and the sprung mass on the spring and damper. The accuracy depends on the model used. A full-vehicle, half-vehicle or quarter-vehicle model may be used. The damping coefficient is represented by a nonlinear damping function. The nonlinear damping function describes the nonlinear relationship between damper force and damper velocity. The nonlinear damping function may model the type of damper curve 500 described above and shown in FIG. 5. Depending on accuracy requirements, the model may or may not further model tyre sidewall stiffness.

Block 906 comprises generating a spectrum of the suspension response output, by transforming the output signal into the frequency domain. In an implementation, the method comprises dividing output force by input force and then block 906 applies a frequency transform to the result. This generates a first spectrum indicating suspension force transmissibility (gain or 'T' in FIG. 6) as a function of frequency. In a non-limiting implementation, the frequency transform technique used can comprise a Fourier transform, such as Fast Fourier Transform (FFT) or direct Fourier Transform.

In later steps, the first spectrum from the blocks 902-906 is plotted against further spectra generated from blocks 912-916 and 922-926.

To summarise, block 912, 914 and 916 are the same as blocks 902, 904 and 906, respectively, except the disturbances in the input signal have a different statistical distribution.

In summary, block 914 comprises determining, via the computer-implemented simulation, a time-varying second suspension response output (second output signal), in response to a time-varying second suspension disturbance input (second input signal) from data block 912.

The second suspension disturbance input comprises disturbances falling within a second statistical distribution different from the first statistical distribution.

The second statistical distribution may comprise a different statistical amplitude distribution than the first statistical amplitude distribution. In an implementation, the second statistical amplitude distribution may be as shown in FIG. 8 and described. To generate the second statistical amplitude distribution, the road factor may have a second multiplier value, greater than the value used for the first simulation at block 904. Therefore, the second input signal is an amplitude-scaled version of the first input signal. In another implementation, the second input signal could be an amplitude-scaled version of a different input signal than the first input signal. The second input signal could be any signal that represents a second road roughness.

In the second simulation of block 914, parameters other than the statistical amplitude distribution may be kept constant, such as the vehicle speed and the simulated suspension system. The same nonlinear damping coefficient function is used. Block 914 may be substantially the same as block 904, with just the input signal being different.

Block 916 comprises generating a spectrum of the suspension response output by transforming the second output signal into the frequency domain, in the same manner as block 906.

Further simulations may be desired, based on further statistical distributions of disturbances, to ensure that a plurality of road roughnesses are tested. Therefore, blocks 922, 924 and 926 are shown. These blocks represent the same steps as blocks 902, 904 and 906 or 912, 914 and 916, applied to the nth statistical distribution, where n is the number of repeats desired.

It is useful if at least one of the simulations is based on a statistical amplitude distribution comprising disturbances of a high enough amplitude to cause the nonlinear damping coefficient function to enter a nonlinear region (enter the second magnitude range of FIG. 5).

The repeat simulations of blocks 912-916 and 922-926 may be performed either manually or automatically. The control system 200 of FIG. 2 discloses structure enabling either manual or automatic implementation of the repeated simulations. Performing another simulation automatically can comprise executing a computer program that is configured to automatically modify the statistical distribution of the disturbances between each simulation. The computer program may change the road factor (scaling factor) automatically and automatically repeat the simulation.

Once the simulations have been completed, the method 900 progresses to block 930. Block 930 comprises causing graphical output of the spectra of blocks 906, 916, and 926, on a common set of axes. See for example FIGS. 10 and 11, which represent the graphical output for a front suspension and a rear suspension, respectively. Plotting the graphical output may be performed manually or automatically. The graphical output may be rendered on the electronic display 216. The spectra may be smoothed and aligned beforehand, in a manner known in the art.

FIG. 10 illustrates transmissibility (gain) on the y-axis and frequency on the x-axis. The y-axis could be a linear scale or a log scale. The frequency range of interest in this case is the primary ride frequency (0.75-1.2Hz), but it could be another frequency. Each spectrum comprises a peak within the primary ride frequency range, representing the maximum force transmissibility.

Block 932 comprises comparing force transmissibility peaks of the different spectra. The comparison may be aided by plotting a line of maxima approximately intersecting the maximum force transmissibility peaks of the spectra. The peaks may be primary ride frequency peaks, as shown. A regression technique may be used, such as linear regression or nonlinear regression (polynomial, etc). The line of maxima indicates the drift of the frequency at which peak transmissibility occurs.

FIG. 10 illustrates the line of maxima 1000 for the front suspension of a simulated vehicle suspension system. The line of maxima 1000 indicates that as the road factor increases, the frequency at which peak transmissibility occurs decreases for the front suspension.

FIG. 11 illustrates the line of maxima 1100 for the rear suspension of the simulated vehicle suspension system. The line of maxima 1100 indicates that as the road factor increases, the frequency at which peak transmissibility occurs decreases for the rear suspension.

Block 934 of the method 900 comprises modifying a suspension design parameter based on the comparison of the results.

The modification can comprise choosing a different spring stiffness or damping coefficient for at least one spring and/or damper. If the method 900 is applied to the design of an active suspension, a controllable variable stiffness and/or a controllable variable damper curve 500 may be tuned.

For example, If the peak transmissibility of the road factors is high, that means the loads going into the suspension system are not being managed. To remedy this issue, the designer could: -Increase the damping via scaling the whole damper; -Increase the bump portion only or the rebound portion only; -Change the knee point of the damper so that the knee point occurs at lower velocities; and/or -Add/change the damping technology (e.g., switching damper valve type or adding in active damping systems or hydraulic compression stops) to increase damping.

The designer could also target regions of the damper curve 500. For example, if road factors 2.0, 2.5 and 3.0 have low transmissibility factors with damper velocities below the knee point, but road factors 3.5, 4 and 4.5 have high transmissibility factors, the designer could increase the high-speed damping in bump, rebound or bump and rebound, but leave the low-speed region unchanged.

If the peak transmissibility of the graphs in general is low, this could indicate that there is an opportunity to improve ride comfort. The designer could: -Decrease the damping via scaling the whole damper, the bump portion only or the rebound portion only; -Change the knee point of the damper so that the knee point occurs at higher velocities; and/or -Potentially remove expensive active damping technology or switch to a lower performing or passive alternative.

Similarly, if road factors 2.0, 2.5 and 3.0 have peak transmissibility below a threshold but road factors 3.5, 4.0 and 4.5 are above an acceptable transmissibility threshold, the designer could choose to decrease the low speed bump, rebound, or bump and rebound low speed damping but leave the high-speed damping region unchanged.

The simulations could then be repeated for the modified suspension design parameter if desired, by updating the simulated suspension system to incorporate the modified suspension design parameters and iterate the method 900 until the NVH (noise, vibration, harshness) and/or vehicle controllability attribute targets are satisfied.

Block 936 comprises outputting to a file the results of the method 900. This can comprise storing the results of the method 900 in the electronic memory 206. The stored results may comprise one or more of the spectra and/or the graphical output and/or the line of maxima. The stored results may comprise the suspension design parameters that were used in one or more of the simulations. In some implementations, the stored suspension design parameters may be provided to a suspension supplier which can then implement the desired suspension design parameters. In some implementations, the suspension design parameters may be stored in the electronic memory of an active suspension controller, if the suspension is an active suspension.

The method 900 may then comprise implementing suspension design parameters in a vehicle suspension system, wherein the suspension design parameters were determined according to the method 900.

It is to be understood that the or each controller 201 can comprise a control unit or computational device having one or more electronic processors (e.g., a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), etc.), and may comprise a single control unit or computational device, or alternatively different functions of the or each controller 201 may be embodied in, or hosted in, different control units or computational devices. As used herein, the term "controller," "control unit," or "computational device" will be understood to include a single controller, control unit, or computational device, and a plurality of controllers, control units, or computational devices collectively operating to provide the required control functionality. A set of instructions could be provided which, when executed, cause the controller 201 to implement the control techniques described herein (including some or all of the functionality required for the method 900 described herein). The set of instructions could be embedded in said one or more electronic processors of the controller 201; or alternatively, the set of instructions could be provided as software to be executed in the controller 201. A first controller or control unit may be implemented in software run on one or more processors. One or more other controllers or control units may be implemented in software run on one or more processors, optionally the same one or more processors as the first controller or control unit. Other arrangements are also useful.

In the example illustrated in Figure FIG. 2, the or each controller 201 comprises at least one electronic processor 204 having one or more electrical input(s) 210 for receiving one or more input signals from an input device 214, and one or more electrical output(s) 212 for outputting one or more output signals to an output device 216. The or each controller 201 further comprises at least one memory device 206 electrically coupled to the at least one electronic processor 204 and having instructions 208 stored therein. The at least one electronic processor 204 is configured to access the at least one memory device 206 and execute the instructions 208 thereon so as to execute at least part of the method 900 of FIG. 9.

The, or each, electronic processor 204 may comprise any suitable electronic processor (e.g., a microprocessor, a microcontroller, an ASIC, etc.) that is configured to execute electronic instructions. The, or each, electronic memory device 206 may comprise any suitable memory device and may store a variety of data, information, threshold value(s), lookup tables or other data structures, and/or instructions therein or thereon. In an embodiment, the memory device 206 has information and instructions for software, firmware, programs, algorithms, scripts, applications, etc. stored therein or thereon that may govern all or part of the methodology described herein. The processor, or each, electronic processor 204 may access the memory device 206 and execute and/or use that or those instructions and information to carry out or perform some or all of the functionality and methodology describe herein.

The at least one memory device 206 may comprise a computer-readable storage medium (e.g. a non-transitory or non-transient storage medium) that may comprise any mechanism for storing information in a form readable by a machine or electronic processors/computational devices, including, without limitation: a magnetic storage medium (e.g. floppy diskette); optical storage medium (e.g. CD-ROM); magneto optical storage medium; read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g. EPROM ad EEPROM); flash memory; or electrical or other types of medium for storing such information/instructions.

Example controllers 201 have been described comprising at least one electronic processor 204 configured to execute electronic instructions stored within at least one memory device 206, which when executed causes the electronic processor(s) 204 to carry out the method as hereinbefore described. However, it will be appreciated that embodiments of the present invention can be realised in any suitable form of hardware, software or a combination of hardware and software. For example, it is contemplated that the present invention is not limited to being implemented by way of programmable processing devices, and that at least some of, and in some embodiments all of, the functionality and or method steps of the present invention may equally be implemented by way of non-programmable hardware, such as by way of non-programmable ASIC, Boolean logic circuitry, etc. It will be appreciated that various changes and modifications can be made to the present invention without departing from the scope of the present application.

The blocks illustrated in FIG. 9 may represent steps in a method and/or sections of code in the computer program 208. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some steps to be omitted.

Although embodiments of the present invention have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the invention as claimed.

Features described in the preceding description may be used in combinations other than the combinations explicitly described. Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not. Although features have been described with reference to certain embodiments, those features may also be present in other embodiments whether described or not.

Whilst endeavouring in the foregoing specification to draw attention to those features of the invention believed to be of particular importance it should be understood that the Applicant claims protection in respect of any patentable feature or combination of features hereinbefore referred to and/or shown in the drawings whether or not particular emphasis has been placed thereon.

Claims (15)

1. CLAIMS1. A method of designing a vehicle suspension system, the method comprising: determining, via computer-implemented simulation, a time-varying first suspension response output, in response to a time-varying first suspension disturbance input, based on a simulated suspension system having a nonlinear damping function, the first suspension disturbance input comprising disturbances falling within a first statistical distribution; determining, via computer-implemented simulation, a time-varying second suspension response output, in response to a time-varying second suspension disturbance input, based on the same simulated suspension system having the same nonlinear damping function, the second suspension disturbance input comprising disturbances falling within a second statistical distribution different from the first statistical distribution; and generating spectra of each of the first and second suspension response outputs, each of the spectra indicating suspension force transmissibility as a function of frequency.
2. 2. The method of claim 1, wherein the second statistical distribution comprises a different statistical amplitude distribution than the first statistical distribution.
3. 3. The method of claim 2, wherein the second suspension disturbance input is an amplitude-scaled version of the first suspension disturbance input.
4. 4. The method of claim 2 or 3, wherein at least one of the first and second statistical amplitude distributions comprises disturbances of a high enough amplitude to cause the nonlinear damping function to enter a nonlinear region.
5. 5. The method of any preceding claim, wherein the first and second suspension response outputs are determined based on a same vehicle speed.
6. 6. The method of any preceding claim, wherein the disturbances of the first and/or second suspension disturbance input are substantially randomly-generated, and configured to fall within the first statistical distribution.
7. 7. The method of any preceding claim, wherein the disturbances of the first and/or second suspension disturbance input are at a plurality of different frequencies.
8. 8. The method of any preceding claim, wherein generating the spectra comprises performing a Fourier transform of each of the first and second suspension response outputs.
9. 9. The method of any preceding claim, comprising: determining, via computer-implemented simulation, one or more further time-varying suspension response outputs, each in response to a different time-varying suspension disturbance input, each based on the same simulated suspension system having the same nonlinear damping function, each different suspension disturbance input comprising disturbances falling within a different statistical distribution than each other and than the first and second statistical distributions; wherein generating the spectra comprises generating further spectra of each of the further suspension response outputs, each of the further spectra indicating suspension force transmissibility as a function of frequency.
10. 10. The method of any preceding claim, comprising causing graphical output of the spectra on a common set of axes.
11. 11. The method of any preceding claim, comprising: comparing force transmissibility peaks of the different spectra; modifying at least one suspension design parameter based on the comparison; and outputting to a file the results of the method.
12. 12. The method of claim 11, comprising: plotting a line approximately intersecting the maximum force transmissibility peaks of the spectra; wherein modifying the at least one suspension design parameter is based on the line intersecting the maximum force transmissibility peaks.
13. 13. The method of claim 11 or 12, wherein the suspension design parameter comprises a passive damping coefficient or an active damping coefficient control parameter.
14. 14. A vehicle suspension system having suspension parameters designed according to the method of any preceding claim.
15. 15. A vehicle comprising the vehicle suspension system of claim 14.