Browse Topic: On-board energy sources
Aluminum alloy wheels have become the preferred choice over steel wheels due to their lightweight nature, enhanced aesthetics, and contribution to improved fuel efficiency. Traditionally, these wheels are manufactured using methods such as Gravity Die Casting (GDC) [1] or Low Pressure Die Casting (LPDC) [2]. As vehicle dynamics engineers continue to increase tire sizes to optimize handling performance, the corresponding increase in wheel rim size and weight poses a challenge for maintaining low unsprung mass, which is critical for ride quality. To address this, weight reduction has become a priority. Flow forming [3,4], an advanced wheel rim production technique, which offers a solution for reducing rim weight. This process employs high-pressure rollers to shape a metal disc into a wheel, specifically deforming the rim section while leaving the spoke and hub regions unaffected. By decreasing rim thickness, flow forming not only enhances strength and durability but also reduces overall
The purpose of this research is to examine the fundamental principles of a circular economy (CE) in relation to the automotive industry in India, which plays a vital role in the country's economy. As a result, energy consumption and environmental impacts also pose significant challenges. CE provide a transformative approach through the life cycle of a vehicle, guiding the automotive industry toward a more sustainable transportation system. In order to decarbonize this industry, the global automotive commission recommends that recycled plastic content in vehicles be increased to 20-25% by 2030. This target necessitates the recovery of plastics from end-of-life vehicles, though these materials are rarely integrated into compounds today. The automotive industry's reliance on plastics has grown substantially due to their lightweight properties, which enhance fuel efficiency, reduce CO₂ emissions, and improve versatility and mechanical performance. polypropylene polymer and several other
The globe is looking headlong to set up new benchmarks for the reduction of GHG (Green House Gases) considering short-term and long-term strategies. Efforts in the Internal Combustion Engines (ICE) domain have been accelerating to find an alternative way to reduce harmful emissions. Hydrogen is considered as a promising fuel to leapfrog this transition. Hydrogen fuel can be categorized into vast mobility areas viz. ICE and Fuel Cell Electric Vehicle (FCEV). Hydrogen fuel has attracted global attention from engine researchers due to the crude oil crisis and its rise in prices in recent years. This will serve the nation's goal towards carbon neutrality. Hydrogen has a few advantages such as less fueling time, higher heating value and more efficiency making it an eye-touching fuel for the automotive industry. In the contemporary FCEV segment, many fuel cell technologies have evolved, wherein the development of Proton Exchange Membrane (PEM) fuel cell technology has taken a new height for
Hydrogenated nitrile butadiene rubbers (HNBR) and their derivatives have gained significant importance in automotive compressed natural gas (CNG) valve applications. In one of the four-wheelers, CNG valve application, HNBR elastomeric diaphragms are being used for their excellent sealing and pressure regulation properties. The HNBR elastomeric diaphragm was developed to sustain CNG higher pressure However, it was found permanently deformed under lower pressures. In this research work, number of experiments was carried out to find out the primary root cause of diaphragm permanent deformation and to prevent the failure for safe usage of the CNG gas. HNBR diaphragm deformation investigation was carried out using advanced qualitative and quantitative analysis methods such as Soxhlet Extraction Column, Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), Optical Microscopy (OM), Scanning Electron Microscopy (SEM), and Thermogravimetric Analysis (TGA). For
The present work demonstrates a transient Fluid-Structure-Interaction (FSI) based numerical methodology for estimation of aerodynamic-induced flutter of the rear bumper of a Sports Utility Vehicle (SUV). Finite Volume Method (FVM) based High-fidelity transient full vehicle aerodynamic simulations were conducted for the estimation of the transient aerodynamic load. Subsequently, by mapping this transient aero load onto the surface of the rear bumper, Finite Element Method (FEM) based dynamic structural simulations were performed to predict its response. The results obtained through simulations were then compared against experimental wind tunnel test data of a prototype car with modified bumper for the specific test-case. The pressure and the time series data of rear bumper deflection were captured at multiple probe locations from wind tunnel experiments at 140 and 200 kmph. The distribution of pressure on the rear surfaces of the car was well captured by the aerodynamic simulation at
This study investigates the phenomenon of receptacle icing during Compressed Natural Gas (CNG) refueling at filling stations, attributing the issue to excessive moisture content in the gas. The research examines the underlying causes, including the Joule-Thomson effect, filter geometries, and their collective impact on flow interruptions. A comprehensive test methodology is proposed to simulate real-world conditions, evaluating various filter types, seal materials and moisture levels to understand their influence on icing and flow cessation. The findings aim to offer ideas for reducing icing problems. This will improve the reliability and safety of CNG refueling systems.
In automotive engineering, understanding driving behavior is crucial for decision on specifications of future system designs. This study introduces an innovative approach to modeling driving behavior using Graph Attention Networks (GATs). By leveraging spatial relationships encoded in H3 indices, a graph-based model constructed, which captures dependencies between various vehicle operational parameters and their operational regions using H3 indices. The model utilizes CAN signal features such as speed, fuel efficiency, engine temperature, and categorical identifiers of vehicle type and sub-type. Additionally, regional indices are incorporated to enrich the contextual information. The GAT model processes these heterogeneous features, learning to identify patterns indicative of driving behavior. This approach offers several significant advantages. Firstly, it enhances the accuracy of driving behavior modeling by effectively capturing the complex spatial and operational dependencies
Over the past few decades, Compressed Natural Gas (CNG) has gained popularity as an alternative fuel due to its lower operating cost compared to gasoline and diesel, for both passenger and commercial vehicles. In addition, it is considered more environmentally friendly and safer than traditional fossil fuels. Natural gas's density (0.7–0.9 kg/m3) is substantially less than that of gasoline (715–780 kg/m3) and diesel (849–959 kg/m3) at standard temperature and pressure. Consequently, CNG needs more storage space. To compensate for its low natural density, CNG is compressed and stored at high pressures (usually 200-250 bar) in on-board cylinders. This results in an effective fuel density of 180 kg/m3 at 200 bar and 215 kg/m3 at 250 bar. This compression allows more fuel to be stored, extending the vehicle's operating range per fill and minimising the need for refuelling. Natural Gas Vehicles (NGVs), particularly those in the commercial sector like buses and lorries, need numerous CNG
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