Development Of Vehicle Models Research Articles

Development of Vehicle Models for Steering Systems

Development of Vehicle Models for Steering Systems

ORES: a chassis dynamometer for off-road vehicles

Off-Road Environment Simulator (ORES) is a Real-time Hardware-in-the-Loop (RT-HIL) platform to simulate the dynamic response of off-road vehicles. This paper primarily focuses on the vehicle model development and validation using both field and rig testing using the ORES platform. Off-road vehicles are capable of operating on bumpy terrains where they are subjected to different resistive wheel torques due to non-unique ground friction conditions and wheel loads. If the powertrain torque output is not distributed in accordance with the resistive wheel torques, it may lead to transmission windup resulting in premature failure of various driveline components. In this study, the vehicle is driven over discrete bumps both in rig simulation and field trial. Different terrain enveloping models were evaluated namely the single point, radial-spring contact model and two-point follower (using circular and sinusoidal basis). These models were evaluated against the measured wheel acceleration responses. The two-point follower with sinusoidal basis strongly correlates with the measured responses and the ground excitations so obtained were used as inputs to a seven degree-of-freedom vehicle ride model. Ride model calculates the wheel loads and is eventually integrated with longitudinal dynamics, tire, driveline and test-rig models. Vehicle axle acceleration, wheel speed and drive torque responses are measured for validating the simulation results against field and rig trials. The field responses matches fairly well which validates the suitability of the proposed modeling approach.

Extending Behavioral Models to Generate Mission-Based Driving Cycles for Data-Driven Vehicle Development

Driving cycles are nowadays, to an increasing extent, used as input to model-based vehicle design and as training data for development of vehicle models and functions with machine learning algorithms. Recorded real driving data may underrepresent or even lack important characteristics, and therefore there is a need to complement driving cycles obtained from real driving data with synthetic data that exhibit various desired characteristics. In this paper, an efficient method for generation of mission-based driving cycles is developed for this purpose. It is based on available effective methods for traffic simulation and available maps to define driving missions. By comparing the traffic simulation results with real driving data, insufficiencies in the existing behavioral model in the utilized traffic simulation tool are identified. Based on these findings, four extensions to the behavioral model are suggested, staying within the same class of computational complexity so that it can still be used in a large scale. The evaluation results show significant improvements in the match between the data measured on the road and the outputs of the traffic simulation with the suggested extensions of the behavioral model. The achieved improvements can be observed with both visual inspection and objective measures. For instance, the 40% difference in the relative positive acceleration of the originally simulated driving cycle compared to real driving data was eliminated using the suggested model.

Application of global sensitivity analysis to statistical energy analysis: Vehicle model development and transmission path contribution

This paper focuses on prediction of the sound pressure level inside a vehicle by using statistical energy analysis (SEA) and control of the noise within limited trim materials for vehicle weight reduction. To this end, a vehicle SEA model was developed considering the effects of the SEA parameter uncertainties on the prediction results. Thereafter, the contributions of the sound transmission losses of several vehicle components and the absorptivity of the vehicle interior to the sound pressure level were identified. Global sensitivity analysis was performed to evaluate both the effects of the SEA parameter uncertainties and the contribution of the acoustical performance. Only the most influential SEA parameters were calculated or measured to reflect the characteristics of the acoustic material. Our results revealed that the sound pressure levels calculated by using the simplified SEA model agreed well with the measured values. Finally, based on the influences of the acoustic performances of the vehicle components on the interior noise, the design of the trim materials on the dash, which was one of the sensitive components, was modified and compared to the initial design and conventional lightweight design in terms of interior noise and weight.

Vehicle Plant Model and Supervisory Control Development for a Parallel Pre-Trans Plug-In Hybrid Electric Vehicle

The Arizona State University EcoCAR-3 team is a new team in Advanced Vehicle Technology Competitions (AVTCs), competing among 16 North American Universities under a 4 year competition aimed at redesigning a high performance conventional vehicle into a hybrid electric vehicle, reducing the environmental impacts while maintaining the performance of the vehicle. The team imitates General Motors (GM) Vehicle Development Process (VDP) in designing, building and refining the vehicle. The first step in design process is Powertrain Architecture selection, and the team is designing a parallel pre-transmission plug-in hybrid vehicle to meet the competition targets. The proposed powertrain consists of a 2.4L gasoline (E85) engine, a 140 kW electric motor, 6-speed automatic gearbox and 18.9 kWh battery pack. The vehicle is capable of delivering 30 mile all-electric operation, along with an impressive Utility Factor weighted fuel economy of 50 mpgee.This paper presents the vehicle model development done by the Arizona State University EcoCAR-3 team in year-1 of the competition. The paper explains the detailed vehicle architecture, modeling platform, technical goals of the team and vehicle performance and energy consumption calculations. Control strategies for power flow management, as well as control system development has been addressed, along with approaches used to address any modeling imperfection.

The influence of triggers geometry upon the stiffness of cylindrical thin walled tubes

AbstractToday’s automobile manufacturers are increasingly using lightweight materials to reduce weight; these include plastics, composites, aluminium, magnesium alloys, and also new types of high strength steels. Many of these materials have limited strength or ductility, therefore in many cases the rupture being serious consequences during crashes, underscore Picketta et al. in their studies.Automotive structures must deform plastically in a short period of time, a few milliseconds, to absorb the crash energy in a controllable manner. It must be light and enable economically mass-production [1].FE models rapidly gained acceptance among engineers. Many other factors facilitated the development of vehicle models by shell finite elements since most of the geometry of the structural surfaces was already on computer graphic files.Kee Poong Kim and Hoon Huh emphasize that the crashworthiness of each vehicle part needs to be evaluated at the initial stage of design for good performance of an assembled vehicle. As the dynamic behaviour of structural members is different from the static one, the crashworthiness of the vehicle structures has to be assessed by impact analysis.The paper analyzes the influence of trigger geometry upon the compression of thin-walled cylindrical tubes. Simulations performed on a simple model showed the dependence between triggers area and deformation times as well as the maximum deformations obtained for various speeds at which the simulations ware carried out. Likewise, the geometry of trigger leads to different results.

Identification of validated multibody vehicle models for crash analysis using a hybrid optimization procedure

The design of components, or particular safety devices, for road vehicles often requires that many analyses are performed to appraise different solutions. Multibody approaches for structural crashworthiness provide alternative methodologies to the use of detailed finite element models reducing calculation time by several orders of magnitude while providing results with the necessary quality. The drawbacks on the use of multibody approaches for crashworthiness are the cumbersome model development process and difficulty of model validation. This work proposes an optimization procedure to assist multibody vehicle model development and validation. First the topological structure of the multibody system is devised representing the structural vehicle components and describing the most relevant mechanisms of deformation. The uncertainty in the model development resides on the constitutive behavior of the plastic hinges used to represent the structural deformations of the vehicle, identified by using the classic multibody approach. The constitutive relations are identified by using an optimization procedure based on the minimization of the deviation between the observed response of the vehicle model and a reference response, obtained by experimental testing or using more detailed models of the vehicle. The identification is supported by a genetic optimization algorithm that enables the characterization of several initial sets of parameters that characterize the plastic hinge constitutive relations in the vicinity of an Edgeworth-Pareto front. Afterwards, a deterministic optimization algorithm is used to identify the accurate minimum. The procedure is demonstrated by the identification of the multibody model of a large family car suitable for front impact.

A Fuelcell Hybrid Electronic Vehicle Model Development Using Functional Suspension Model

A Fuelcell Hybrid Electronic Vehicle Model Development Using Functional Suspension Model