Parameters uncertainty in pareto optimization of nonlinear inerter-based suspension system under nonstationary random road excitation

Ignoring the possible impacts of uncertainties in vehicle components during the design phase can undermine the safety of passengers and the vehicle performance. The main function of a suspension system is to provide satisfactory ride comfort and road-holding with a sufficiently low probability of rollover. Despite many studies on the design of new suspension systems with inerters, the effect of uncertainties in vehicle weight and tire stiffness on the design of suspension with inerters has not received much attentions. This paper presents a new type of suspension with inerters and asymmetric dampers and investigates the dynamic behavior of a vehicle under variable vehicle speed. Moreover, the effect of uncertainties on the choice of acceptable values of inerters is evaluated. For this investigation, the authors developed a 9-DOF full vehicle model with roll and yaw motions under non-stationary random road excitations in the time and frequency domains and studied its dynamic response with different suspension models. The optimal design was performed using a multi-objective optimization algorithm called MOEA/D. The best model was then used to determine the effect of uncertainties on the choice of inerters. The optimization results show that using the optimized suspension with inerters and nonlinear dampers instead of conventional design improves the ride comfort by 0.16%, the vehicle road-holding by 3.54%, and the rollover probability by 44.73%. In the proposed model, by changing the values of vehicle parameters with uncertainty, the choice of inerters to have an acceptable performance would be variable.

New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

This paper presents a new indirect tire pressure monitoring system (TPMS) based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested methodology is based on the explicit correlation between tire pressure and tire stiffness and is available in real time. AEKF-UI is used to simultaneously estimate the time-varying parameter (tire stiffness) of vehicle suspension systems and the road roughness using an unknown input estimator. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate tire stiffness (i.e., tire inflation pressure) variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm are verified through a laboratory-level experiment. This study offers a potential application for an alternative indirect TPMS and the estimation of unknown road roughness used for automotive controller design.

Development of a Simulated Three-Dimensional Truck Model to Predict Excess Fuel Consumption Resulting from Pavement Roughness

Excess vehicle fuel consumption, the percentage change in fuel consumption caused by road roughness, is an important part of the environmental and cost assessment of a pavement’s life cycle. Flexible and efficient computational methods make it possible to use mechanistic models to estimate excess fuel consumption. A stochastic pavement–vehicle interaction model was developed recently based on a half-truck model and stationary road roughness profiles. Although the introduced method was a step forward, it does not allow roll vibration. In addition, the error caused by stationary assumption on roughness profiles has not been quantified. This study proposes a numerical approach to assess the roughness-induced fuel consumption of a semi-trailer truck on non-deformable rough pavements. A three-dimensional (3D) semi-trailer truck model is formulated with a nonstationary parallel road roughness model. The simulation results of the integrated truck–pavement model are validated against empirical formulas. Tire stiffness is identified as the most important truck property, followed by suspension damping, suspension stiffness, and cargo loading. For road roughness characteristics, local roughness variance—overlooked by the stationary assumption—can underestimate excess fuel consumption by 42%. Using the 3D truck model and corresponding roughness profiles as excitation inputs would reduce the computation error by more than 10%. This study also proposes an extended roughness–speed–impact model, considering a second-order International Roughness Index term and effect of local roughness variance. The new regression model increases the prediction explanation R2 from 88.7% to 99.2%.

Conflict and Sensitivity Analysis of Vehicular Stability Using a Two-State Linear Bicycle Model

Vehicle parameters and operation conditions play a critical role in vehicular handling and stability. This study aimed to evaluate vehicle stability based on cornering tire stiffness integrated with vehicle parameters. A passenger vehicle is considered in which a two-state linear bicycle model is developed in the Matlab/Simulink. The effect of the vehicle parameters on lateral vehicle stability has been investigated and analyzed. The investigated parameters included CG longitudinal position, wheelbase, and tire cornering stiffness. Furthermore, the effects of load variation and vehicle speed were addressed. Based on a Fishhook steering maneuver, the lateral stability criteria represented in lateral acceleration, yaw rate, vehicle sideslip angle, tire sideslip angles, and the lateral tire force were analyzed. The results demonstrated that the parameters that affect the lateral vehicle stability the most are the cornering stiffness coefficient and the CG longitudinal location. The findings also indicated a positive correlation between vehicle properties and lateral handling and stability.

Sensitivity analysis of three-wheel vehicle’s suspension parameters influencing ride behavior

In this article coupled vertical–lateral 9 degree-of-freedom model of a three-wheel vehicle formulated using Lagrangian dynamics is presented in order to determine its vertical and lateral ride. The model is justified by correlating the power spectral density vertical and lateral acceleration results determined from analysis with the same obtained from experimental measurements. The ride comfort of the vehicle is evaluated on the basis of ISO 2631-1 criteria. The sensitivity of vehicle suspension parameters on vertical and lateral ride behavior is analyzed, and it is noticed that rear-suspension damping coefficient, front-tire stiffness, rear-tire stiffness, front-tire damping coefficient, and rear-tire damping coefficient are critical parameters for vertical power spectral density acceleration. Rear-suspension stiffness, rear-suspension damping coefficient, front-tire stiffness, and rear-tire stiffness are critical parameters for lateral power spectral density acceleration.