Magic Numbers in Design of Suspensions for Passenger Cars

Barak, Pinhas

doi:10.4271/911921

Cited by 40 publications

(15 citation statements)

References 14 publications

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“…The Etman et al 12 damper model is adopted to model the non-linear damper profiles measured from the experimental data. The model parameterizes a non-linear curve into an empirical relation controlled by eight variables (1). The model consists of a similar function used in describing the compression and the rebound.…”

Section: Ride Modelmentioning

confidence: 99%

“…The conventional workflow of suspension tuning is target oriented where the suspension design performance goals are determined through analysis of benchmark vehicles. 1 It is then followed by multiple stages of the optimization process until the specified design targets are achieved. The drawbacks of this method are that it is time consuming and the exploration of design changes in the suspension system is restricted to predefined targets.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, suspension tuning is based on trial and error with some guidance based on the rule of thumb proposed by Olley 2 and the magic numbers based on past experience as proposed by Barak. 1 These settings or criteria defined by Olley and by Barak are widely referred to in the design and tuning of passive suspension systems. However, these only serve as references because translating them into an exact design of suspension through the trial-and-error method is difficult and time consuming.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the uncertainties in the achievable vehicle performance targets through design optimization

Tey

Ramli

2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper an alternative method is proposed for defining the suspension performance targets through the use of fullvehicle modelling consisting of a ride model and a handling model. These models are derived with the use of a non-linear damper, suspension kinematic characteristics and basic vehicle dimensions. The vehicle performances can be explored using the design-of-experiments method. The non-sorting method is then employed to sort for non-dominated solutions, where these samples represent the Pareto front of the vehicle performances in ride comfort and handling. The k-means clustering method is used to classify further the solution into different unique optimum characteristics. The expectation-maximization algorithm is developed to compute the allowable variance of design parameters required to achieve the specific optimum design targets. This method can be a very useful tool in the earliest design stages where vehicle data are inadequate. This methodology potentially reduces the uncertainty in the achievable vehicle performance targets by allowing engineers to compare the optimum limit of the suspension with those of benchmark vehicles in the early suspension design and development process

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Section: Ride Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reducing the uncertainties in the achievable vehicle performance targets through design optimization

Tey

Ramli

2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…1a. The vertical forces are affected by the square of the motion ratio of the damper and spring, (η), Barak [1991], and reflect the projection of the force to the vertical axis in the wheel. A nonlinear single corner model with a passive damper will be used as a baseline model:…”

Section: Modelsmentioning

confidence: 99%

Adaptive Semi-Active Suspension Design Using Gain-Scheduling

Lozoya-Santos

Ramírez-Mendoza

Sename

et al. 2013

IFAC Proceedings Volumes

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A novel approach to design a road adaptive semi-active suspension is proposed. Two scheduling parameters are considered: the road frequency and the type of road. A single corner vehicle model with a nonlinear dynamic model of the semi-active damper is considered. The suspension deflection and its derivative are used as feedback signals. Nonlinear simulations show that an adaptive suspension controller: Frequency-Estimation-Based with road Adaptation (FEBA) provides better comfort and road holding over different types of road. The improvement of the performance ranges from 5-10 %.

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“…In fact, an active system has the capability of saving, producing and damping of the vibration energy of the system. In elaboration, many researchers have been extensively conducted their studies on the suspension systems, variations of roads and the related effects on the steering and passengers’ comfort (Barak, 1991; Griffin et al, 1982; Rakheja, 1985). Bouazara (1997) investigated the effects of the suspension system parameters on the vibration of two, five, and eight degrees of freedom models of a vehicle.…”

Section: Introductionmentioning

confidence: 99%

Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

Mahmoodabadi

Javanbakht

2020

Transactions of the Institute of Measurement and Control

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This paper presents optimal adaptive fuzzy approaches combined by the predictive models for five degrees of freedom vehicle systems having a control force constraint of 1000 N on both front and rear suspensions in order to minimize the road disturbances. First, two separate adaptive fuzzy controllers are designed for the rear and front tires using the singleton fuzzifier, center average defuzzifier and product inference engine. The constructed fuzzy systems implement the adaptation laws based on the Lyapunov theory to guarantee the stability of the system. Afterward, a gravitational search optimization algorithm is applied to calculate the optimal values of the controller’s gains. The weighted summation of four objectives, as the relative displacement between the sprung mass and the front tire, the relative displacement between the sprung mass and the rear tire, the acceleration of the body and the acceleration of the seats, are regarded in the optimization process. Two different predictive models are employed to find the optimal design variables for the circumstances where the stability of the system is under variation. The first model is a fuzzy predictive system while the second one is based on the moving least squares interpolation. Eventually, the resultant online models are compared with the offline systems when the vehicle mass varies. These simulations obviously illustrate the efficiency and ability of the suggested strategy to remove the effect of the road disturbances on the ride comfort.

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Magic Numbers in Design of Suspensions for Passenger Cars

Cited by 40 publications

References 14 publications

Reducing the uncertainties in the achievable vehicle performance targets through design optimization

Reducing the uncertainties in the achievable vehicle performance targets through design optimization

Adaptive Semi-Active Suspension Design Using Gain-Scheduling

Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

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