Browse Topic: Vehicle ride
The electric vehicle market, vehicle ECU computing power, and connected electronic vehicle control systems continue to grow in the automotive industry. The results of these advanced and expanded vehicle technologies will provide customers with increased cost savings, safety, and ride quality benefits. One of these beneficial technologies is the tire wearing prediction. The improved prediction of tire wear will advise a customer the best time to change tires. It is expected that this prediction algorithms will be essential part for both the optimization of the chassis control systems and ADAS systems to respond to changed tire performance that varies with a tire’s wear condition. This trend is growing, with many automakers interested in developing advanced technologies to improve product quality and safety. This study is aimed at analyzing the handling and ride comfort characteristics of the tire according to the depth of tire pattern wear change. The handing and ride comfort
Electric vehicles (EVs) are particularly susceptible to high-frequency noise, with rubber eigenmodes significantly influencing these noise characteristics. Unlike internal combustion engine (ICE) vehicles, EVs experience pronounced variations in dynamic preload during torque rise, which are substantially higher. This dynamic preload variation can markedly impact the high-frequency behaviour of preloaded rubber bushings in their installed state. This study investigates the effects of preload and amplitude on the high-frequency dynamic performance of rubber bushings specifically designed for EV applications. These bushings are crucial for vibration isolation and noise reduction, with their role in noise, vibration, and harshness (NVH) management being more critical in EVs due to the absence of traditional engine noise. The experimental investigation examines how preload and excitation amplitude variations influence the dynamic stiffness, damping properties, and overall performance of
The parametrized twist beam suspension is a pivotal component in the automotive industry, profoundly influencing the ride comfort and handling characteristics of vehicles. This study presents a novel approach to optimizing twist beam suspension systems by leveraging parametric design principles. By introducing a parameter-driven framework, this research empowers engineers to systematically iterate and fine-tune twist beam designs, ultimately enhancing both ride quality and handling performance. The paper outlines the theoretical foundation of parametrized suspension design, emphasizing its significance in addressing the intricate balance between ride comfort and dynamic stability. Through a comprehensive examination of key suspension parameters, such as twist beam profile, material properties, and attachment points, the study demonstrates the versatility of the parametric approach in tailoring suspension characteristics to meet specific performance objectives. To validate the
This SAE Recommended Practice establishes a rating scale for subjective evaluation of vehicle ride and handling. The scale is applicable for the evaluation of specific vehicle ride and handling properties for specified maneuvers, road characteristics and driving conditions, and on proving ground and public roads. The validity of the evaluation is restricted to the individual ride and handling disciplines defined by these maneuvers and to the particular combination of conditions of the vehicle (e.g., equipment, degree of maintenance) and of the environment (e.g., road, weather). This rating scale may not be suitable for some applications, such as specific types of ride or handling qualities, driver populations and market segments, or for correlating with objective measures. Appendix A discusses rating scales that better suit such applications. This document is intended as a guide toward standard practice and is subject to change to keep pace with experience and technical advances.
Making a Miata feel at home off-road takes ingenuity and some help from modern 3D-printing tech. I have always loved off-road racing. I love the innovation, grit and determination it takes to get across the finish line after 250, 500 or even 1,000 miles (402, 805 or 1,609 km) of racing. I have also always loved Miatas. I bought my first NA in 1994 and never looked back. I currently own a 2004 Mazdaspeed Miata and a 2001 lifted Miata.
While there is a tendency for new vehicles to have a focus on ride, handling, performance and other dynamic elements, the model year 2024 Lincoln Nautilus team added another element to how the driver will experience the midsize SUV. Not that the ride, handling, etc. were ignored, but the global design and engineering team wanted to do something different with this two-row SUV. Recognize that this is a vehicle with a sumptuous interior that includes not only first-class seating (24-way adjustable front seats) and materials (Alpine Venetian leather available on the seats; cashmere for the headliner) but also an available high-end Revel Ultima 3D audio system with 28 speakers. What's more, there's “Lincoln Digital Scent,” small electronically activated pods containing various aromas (e.g., Mystic Forest, Ozonic Azure, Violet Cashmere). Across the top of the instrument panel there is a 48-inch backlit LCD screen and a 11.1-inch touchscreen in the center stack.
Designing an automotive seat, it is required to perform a detailed study of anthropometry, which deals with measurement of human individuals and understanding human physical variations. It also requires application-based movement study of driver’s hands, feet’s & overall body movement. It is very difficult to design seat curvatures based on any static manikin-based software. We at VECV, have developed a new concept using mixed reality VR technology to capture all body movements for designing best in class seat curvature to accommodate variety of drivers with different body types. We have designed a specialized static bunk, which has a wide range of seat, steering and ABC paddle adjustments, which are integrated with virtual data. We use to study and capture the data of driving position and other ergonomic postures of wide range of people with different body types on this static bunk according to their comfortable driving posture. In this comfortable driving posture, user is immersed in
Road roughness is the most important source of vertical loads for road vehicles. To capture this during durability engineering, various mathematical models for describing road profiles have been developed. The Laplace process has turned out to be a suitable model, which can describe road profiles in a more flexible way than e.g., Gaussian processes. The Laplace model essentially contains two parameters called C and ν (to be explained below), which need to be adapted to represent a road with certain roughness properties. Usually, local road authorities provide such properties along a road on sections of constant length, say, 100 m. Often the ISO 8608 roughness coefficient or the IRI (International Roughness Index) are used. In such cases, there are well known explicit formulas for finding the parameters C and ν of the Laplace process, which best fits the road under certain assumptions. Besides local road authorities there are also other sources of roughness data, for instance commercial
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