Browse Topic: Vehicles, Equipment, and Performance
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
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
The rise of electric and hybrid vehicles with separate axle or wheel drives enables precise torque distribution between the front and rear wheels. The smooth control of electric motors allows continuous operation on high-resistance roads, optimizing torque distribution and improving efficiency. In hybrid vehicles, synergistic control of both internal combustion engines and electric motors can minimize energy consumption. Using the internal combustion engine for steady driving and electric power for acceleration enhances dynamic performance. Keeping the internal combustion engine at a constant speed is key to improving energy efficiency and vehicle responsiveness. The proposed method aids in selecting optimal power levels for both engines during the design phase. As acceleration time decreases, the ratio of electric motor power to internal combustion engine power increases. The torque distribution system, relying on sensors for axle loads, vehicle speed, and engine power, can reduce
Experimental studies of wind tunnel blockage for road vehicles have usually been conducted in model wind tunnels. Models have been made in a range of scales and tested in a working section of fixed size. More recently CFD studies of blockage have been undertaken, which allow a fixed vehicle size and the blockage is varied by changing the cross section of the flow domain. This has some inherent advantages. A very recent database of CFD derived drag and lift coefficients for different road vehicle shapes and simple bodies tested in a closed wall tunnel with a wide range of blockage ratios has become available and provides some additional insight into the blockage phenomenon. In this paper a process is developed to derive the parameters influencing wind tunnel blockage corrections from CFD data. These are shown to be reasonably effective for correcting the measured drag and lift coefficients at blockage ratios up to 10%.
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