Design of ANFIS networks using hybrid genetic and SVD methods for modeling and prediction of rubber engine mount stiffness

Genetic Algorithm (GA) and Singular Value Decomposition (SVD) are deployed for optimal design of both the Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of ANFIS networks. These networks are used for stiffness modelling and prediction of rubber engine mounts. The aim of such modelling is to show how the stiffness of an engine mount changes with variations in geometric parameters. It is demonstrated that SVD can be optimally used to find the vector of linear coefficients of conclusion parts using ANFIS (Adaptive Neuro-Fuzzy Inference Systems) models. In addition, the Gaussian membership functions in premise parts can be determined using a GA. In this study, the stiffness training data of 36 different bush type engine mounts were obtained using the finite element analysis (FEA).     

Optimized fuzzy control strategy for a spa hybrid truck

In this paper, an optimized control strategy is proposed for a split parallel hydraulic hybrid truck. The model of the vehicle was simulated in Simulink. According to a global optimization technique, a fuzzy control strategy is developed for the vehicle. This strategy shows flexibility for different drive cycles and a desirable fuel consumption reduction, especially for a low speed drive cycle, which is extracted according to an urban utility vehicle mission.     

A simplified model for evaluating tire wear during conceptual design

In the field of vehicle dynamics, commercial software can aid the designer during the conceptual and detailed design phases. Simulations using these tools can quickly provide specific design metrics, such as yaw and lateral velocity, for standard maneuvers. However, it remains challenging to correlate these metrics with empirical quantities that depend on many external parameters and design specifications. This scenario is the case with tire wear, which depends on the frictional work developed by the tire-road contact. In this study, an approach is proposed to estimate the tire-road friction during steady-state longitudinal and cornering maneuvers. Using this approach, a qualitative formula for tire wear evaluation is developed, and conceptual design analyses of cornering maneuvers are performed using simplified vehicle models. The influence of some design parameters such as cornering stiffness, the distance between the axles, and the steer angle ratio between the steering axles for vehicles with two steering axles is evaluated. The proposed methodology allows the designer to predict tire wear using simplified vehicle models during the conceptual design phase.     

La place des modèles de trafic dans les récentes modélisations des impacts environnementaux des transports. Importance de l’explicitation des méthodes et hypothèses

The authors present a recent development in the use of classic travel demand models (TDMs) to environmental impact assessment of transport, far from its initial target. By comparing previous cases found in the literature (Chester, Seoul, Florence, Brisbane and Saint-Etienne) with their present works (Eval-PDU in Nantes), the authors notice that their predecessors tend to be evasive on their use of TDM. Hence, traffic data are little discussed in these works, while their works constitute one of the main stakes in this kind of study. Indeed, the hypotheses for traffic modeling are impacting the next steps of the modeling chain (pollutants emission/dispersion). The importance of this first modeling stage implies that relevant attention has to be brought to their assumptions and input data.     

Automotive transmission efficiency measurement using a chassis dynamometer

Automotive transmission efficiency measurements are usually performed on purpose-built rigs. A simple model was developed for calculating the overall transmission efficiency of passenger cars by using a chassis dynamometer. Wheel power and engine output were measured, and these values were used for calculations. The proposed method can only be employed for vehicles with manual drive because it requires constant speed measurements. Two case studies were investigated, with front-wheel and rear-wheel drive passenger cars. The results obtained from using the proposed model are in good agreement with data provided in the literature.     

Performance measurements of a tracked vehicle system

To improve crossing ability, the most important performance factor for tracked vehicle systems operating on low-bearing capacity peats, and to minimize income losses that result from downtime and maintenance costs, a vehicle was designed in order to adapt to operating condition changes. This article describes the mobile performance of a novel vehicle with segmented rubber tracks on a low-bearing capacity peat. At an equivalent travelling speed, the novel vehicle’s tractive performance in a variable operating environment caused by changes in terrain cohesiveness and hydrodynamic responses was superior to that of the previous model. The new vehicle, which could be operated on the Sepang peat, showed a tractive effort of 42.2% of the gross vehicle weight in field experiments; the recommended minimum tractive effort is between 30 and 36% of the gross vehicle weight.     

Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions

The behaviour of the tyre plays an important role in the vehicle handling. An accurate tyre model that estimates these forces and moments it is highly essential for the studies of vehicle behaviour. For the last ten years neural networks have attracted a great deal of attention in vehicle dynamics and control. Neural networks have been effectively applied to model complex systems due to their good learning capability. In this paper a recursive lazy learning method based on neural networks is considered to model the tyre characteristics under combined braking and cornering. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. Moreover, the neural model proposed allows to include the asymetric tyre behaviour in the tyre model without difficulty.     

Dynamic and Vibration Analysis of a Vehicle Rear Axle System

A detailed finite element model for the rear axle system of a sport utility vehicle is developed in this investigation. The axle system is treated as a multibody system that consists of nine bodies that include the input shaft, two output shafts, the carrier and tube system, four control arms and a track bar. The rotating input and output shafts are mounted on the carrier and tube system using six bearings. The four control arms and the track bar are connected to the carrier system and the frame of the vehicle using rubber bushings. In the model developed in this investigation, three dimensional beam elements are used to develop the finite element model for the input and output axle shafts, the control arms, and the track bar. A non-conventional finite element formulation is used to develop the equations of motion of the rotating input and output shafts in order to account for the effect of their angular velocities. These equations are expressed in terms of inertia shape integrals that depend on the assumed displacement field. The inertia shape integrals are first evaluated for each finite element. The inertia shape integrals of the rotating shafts are obtained by assembling the inertia shape integrals of its finite elements using a standard finite element assembly procedure. A conventional finite element formulation is used for the control arms and the track bar. The model developed in this investigation includes the effect of the bearing stiffness, the effect of the stiffness of the helical springs of the suspension system, and the effect of the stiffness of the tires. Using the Lagrangian dynamics and the finite element method, the equations of motion of the axle system are developed and expressed in terms of the nodal coordinates of the shafts, the control arms and the track bar as well as the degrees of freedom of the carrier. This finite dimensional model is used to determine the mode shapes and the natural frequencies of the axle system. The discrepancies between several of the natural frequencies predicted using the dynamic model developed in this investigation and natural frequencies determined experimentally are found to be less than 2%. A parametric study is performed in order to investigate the effect of the axle system parameters on the natural frequencies and mode shapes. Using the modal transformation, a set of differential equations of motion of the axle system is developed and used to examine the system dynamics under given loading conditions. The solutions of the resulting equations of motion are obtained using numerical methods.     

Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments

Good estimates of vehicle mass and road grade are important in automation of heavy duty vehicles, vehicle following manoeuvres or traditional powertrain control schemes. Recursive least square (RLS) with multiple forgetting factors accounts for different rates of change for different parameters and thus, enables simultaneous estimation of the time-varying grade and the piece-wise constant mass. An ad hoc modification of the update law for the gain in the RLS scheme is proposed and used in simulation and experiments. We demonstrate that the proposed scheme estimates mass within 5% of its actual value and tracks grade with good accuracy provided that inputs are persistently exciting. The experimental setups, signals, their source and their accuracy are discussed. Issues like lack of persistent excitations in certain parts of the run or difficulties of parameter tracking during gear shift are explained and suggestions to bypass these problems are made.     

Application of Sensitivity Analysis to the Development of High Performance Adaptive Hydraulic Engine Mounts

Summary In this paper, the sensitivity analysis is applied to the development of high performance adaptive hydraulic mounts. The analysis allows us to select the most effective design parameters for tuning an adaptive mount to different road and engine conditions. It is shown that in the low frequency road excitation, the upper chamber compliance and inertia of the fluid column in the inertia track are the most influential properties in changing the dynamic stiffness of the hydraulic mount. These properties for the high frequency engine excitations are the upper compliance and the inertia of the fluid column of the decoupler. For tuning the adaptive mount to different road and engine excitation, a global optimization technique is used to find the magnitude of the adjusting parameters to minimize objective functions in low and high frequency excitations. The results indicate significant improvement over conventional hydraulic mounts. It is further shown that when the upper compliance is used as the adjusting parameter, a simple on-off control which is triggered by the engine revolution and vehicle speed is sufficient for tuning the adaptive mount.