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Browse AllTire noise reduction is important for improving ride comfort, especially in electric vehicle due to lack of engine noise and majority of the noise generated in-cabin is from tire-road interaction. Therefore, the tire tread pattern contribution is one of the important criteria for NVH performance apart from other structurally generated noise and vibration. In this work a GUI-based pitch sequence optimization tool is developed to support tire design engineers in generating acoustically optimized tread sequences. The tool operates in two modes: without constraints, where the pitch sequence is optimized freely to reduce tonal noise levels; and with constraints, where specific design rules are applied to preserve pattern consistency and manufacturability. The key point to be considered in this pitch sequence is that it should be reducing the tonal sound and equally spread i.e., the same pitch cannot be concentrated on one side which may lead to non-uniformity. So, the restriction is that
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis
Ambient light reflecting off internal components of the car, specifically the Head-Up Display (HUD), creates unwanted reflections on the Windshield. These reflections can obscure the driver's field of view, potentially compromising safety and reducing visual comfort. The extent of this obscuration is influenced by geometrical factors such as the angle of the HUD and the curvature of the Windshield, which need to be analyzed and managed. The primary motivation is to improve driver safety and visual comfort. This is driven by the need to address the negative impact of ambient light reflecting off Head-Up Displays (HUDs), which can impair visibility through the Windshield. There is a need for tools and methods to address this issue proactively during the vehicle design phase. This study employs a tool-based modeling method to trace the pathways of ambient light from its source, reflecting off the HUD, and onto the Windshield using a dimensional modeling tool. It focuses on: Geometrical
As the brain and the core of the electric powertrain, the traction inverter is an essential part of electric vehicles (EVs). It controls the power conversion from DC to AC between the electric motor and the high-voltage battery to enable effective propulsion and regenerative braking. Strong and scalable inverter testing solutions are becoming more essential as EV adoption rises, particularly in developing nations like India. In India, traditional testing techniques that use actual batteries and e-motors present several difficulties, such as significant safety hazards, inadequate infrastructure, expensive battery prices, and a shortage of prototype-grade parts. This paper presents a comprehensive approach for traction inverter validation using the AVL Inverter TS™ system incorporating an advanced Power Hardware-in-the-Loop (PHiL) test system based on e-motor emulation technology. It enables safe, efficient, and reliable testing eradicating the need for actual batteries or mechanical
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing
The impact configuration has a strong influence on the rear seat survival space intrusion pattern during severe rear-impact collisions. The relative contributions of rear seat pan forward intrusion versus rear seatback intrusion vary depending on the nature of the crash. In underride impacts, the rear wheels are pushed forward into the occupant survival space from below, causing the rear seat-pan to move forward and upward relative to the vehicle interior. Conversely, override impacts tend to produce direct seatback intrusion into the rear compartment. This study used a validated computer model from the NHTSA website to simulate various types of rear compartment intrusions under different impact configurations. The analysis also assessed structural countermeasures designed to minimize occupant survival space intrusion. The results demonstrate that underride impacts primarily drive the forward motion of the rear wheels into the structure, establishing load paths that lead to structural
Higher road noise is perceived in the cabin when the test vehicle encounters road irregularities like bump or pothole in the public roads. The transfer of transient road inputs inside the body caused objectionable cabin noise. Measurements are conducted at different road surfaces to identify the patch where the objective data well correlated with the noise measured at the public road. Wavelet analysis is carried out to identify the frequency zones since the events are transient in nature. TPA is carried out in time domain to identify the nature of the noise and the dominant path through which the transient road forces are transferring inside the body. Based on the outcome of TPA, various countermeasures like reduction of dynamic stiffness of suspension bushes, TMDs on the path are proposed to reduce the structure borne noise. Criteria which need to be considered for reduction of cabin noise due to transient road inputs is also discussed.
Unlike internal combustion engine (IC Engine) vehicles, the rapidly growing electric vehicle (EV) market demands tyres with superior yet often conflicting performance characteristics. The increased weight of EVs, due to their heavy batteries, necessitates robust tyres with reinforcement and higher inflation pressure. Conversely, increased wear due to higher initial torque and the need for lower rolling resistance to extend range, combined with the requirement for better grip for improved handling, call for advanced compound and tread pattern designs. EV tyres need to be stiffer, lighter, and low hysteresis, making it very hard to reduce low-frequency (20-200 Hz) interior noise that was previously masked by engine noise. This study investigates the low-frequency (20-200 Hz) structural-borne interior noise performance of EV tyres using both experimental and simulation tools. By wisely tuning the tyre's stiffness, mass, and damping properties, the necessary noise targets can be achieved