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Browse AllElectric 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 problem of monitoring the parametric failures of a traction electric drive unit consisting of an inverter, a traction machine and a gearbox when interacting with a battery management system has been solved. The strategy for solving the problem is considered for an electric drive with three-phase synchronous and induction machines. The drive power elements perform electromechanical energy conversion with additional losses. The losses are caused by deviations of the element parameters from the nominal values during operation. Monitoring gradual failures by additional losses is adopted as a key concept of on-board diagnostics. Deviation monitoring places increased demands on the information support and accuracy of mathematical models of power elements. We take into account that the first harmonics of currents and voltages of a three-phase circuit are the dominant energy source, higher harmonics of PWM appear as harmonic losses, and mechanical losses in the rotor and gearbox can be
The integration of mobile device data in accident/crash/collision reconstruction methodologies offers significant potential in analyzing collision events. This study evaluates the utility of iPhone-recorded data, specifically Global Navigation Satellite System (GNSS) position and speed data, along with Coordinated Universal Time (UTC) based time and date information associated with application usage and device activity events. By conducting controlled tests, the accuracy, precision, and reliability of iPhone GNSS data were compared against high-accuracy reference systems, including a Racelogic VBox Video HD2 25 Hz GPS data logger and VBox Sport 25 Hz GPS data logger. The synchronicity between recorded app events and device activities with physical events was also analyzed to assess the temporal resolution of the data. Results highlight the strengths and limitations of iPhone data for reconstructing crash events, including potential discrepancies in GNSS accuracy under varying
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