Browse Topic: Fuel economy
The ongoing efforts for reduction of the traffic-related greenhouse gas emissions and, at the same time, the mitigation of harmful pollutant emissions from vehicle exhaust emissions are important development tasks for the entire automotive industry worldwide according to demand to provide clean and efficient products. Further tightened fleet average FE standards and ultra-low limits for exhaust emissions require the continuous development of new propulsion system types. Due to the given reluctance of the end customer and corresponding low acceptance of fully electrified vehicles, especially in the commercial vehicle segment, new and innovative topologies are needed to meet regulatory requirements and maintain the high versatility of today’s dominating solutions. For further optimization of operating conditions with enhanced fuel efficiency, the technical strategy is also determined by uplifting the attractiveness of electric driving incl. the avoidance of areas with poor ICE efficiency
Lightweighting of components has become a key challenge in the development of modern transportation systems. In the automotive and aerospace industries, the overall mass of a vehicle has a significant impact on its fuel efficiency and manufacturing cost. Therefore, the lightweight design of vehicle components is crucial in the industrial field. Topology optimization (TO) is a computational design approach aimed at achieving lightweight designs. However, most existing studies focus on simplified academic models, with limited demonstration in real-world applications. This paper presents a revised TO workflow to obtain production-ready design and a practical implementation of TO in the design of three structural components in the aerospace industry: seatback frame, seat fuselage mount, and seat spreader. The revised TO workflow incorporates the practical demands of industry, including enhanced manufacturability and cost efficiency through TO design. The resulting designs are evaluated to
Aerodynamic simulations are crucial in vehicle design and performance evaluation. Traditionally, these simulations utilize Computational Fluid Dynamics (CFD) techniques to compute flow quantities such as velocity, pressure, and wall-shear stresses. Accurate prediction of these quantities is vital for estimating drag and lift forces, which directly impact fuel efficiency, stability, and acoustics. This study focuses on developing an AI surrogate for aerodynamic design of production mideo-size SUVs using NVIDIA’s PhysicsNeMo framework. Firstly, high-fidelity 3D CFD data are generated using first-principles solvers on 102 different geometry variants at a uniform inlet velocity of 38.89 m/s and a fixed set of boundary conditions. The DoMINO (Decomposable Multiscale Iterative Neural Operator) AI model, part of the PhysicsNeMo framework, is then used to train on this dataset, accurately predicting surface pressure and flow fields around vehicles for rapid estimation of critical aerodynamic
MSIL (Maruti Suzuki India Limited), India’s leading automotive manufacturer, offers a diverse range of SUVs (Sports Utility Vehicles) in its portfolio. Traditionally, SUVs are associated with an assertive stance and a commanding road presence; however, this bold design language often compromises aerodynamic drag performance. Over the past decade, demand for this segment has surged, while CAFE (Corporate Average Fuel Economy) regulations have become increasingly stringent. To address this growing market need, MSIL conceptualized a new SUV - Victoris - targeted to deliver best-in-class aerodynamic efficiency in MSIL SUV portfolio. This paper details the aerodynamic development process using CFD (Computational Fluid Dynamics) and full-scale WTT (Wind Tunnel Testing). Initially, the aggressive styling of Victoris negatively impacted drag performance. Strategic exterior surface refinements and integration of aero components enabled recovery of aerodynamic efficiency. Key interventions
Gasoline direct injection (GDI) engines are the most common technology on American roadways in 2025, and soon, an industrywide gasoline quality standard will better reflect their unique operational needs. Here's why that's important. It's no secret that fuel economy has been one of the greatest driving forces of automotive evolution over the past several decades. As corporate average fuel economy (CAFE) standards have grown increasingly lofty, OEMs eke out new efficiencies from every area of the vehicle. One of those areas, of course, is the engine, and many OEMs have deployed gasoline direct injection (GDI) technology, which is becoming the most common engine technology on American roadways. But while GDI engines proliferate, varying fuel additization throughout North America has not necessarily kept pace with their unique needs and can, in fact, hinder those engines from meeting and sustaining their full fuel economy potential.
Carbon fiber-reinforced polymers (CFRPs) have become essential in modern aerospace structures, from fuselage skins and wing components to nacelles, interior structures, and a growing range of primary load-bearing parts. Their high strength-to-weight ratio delivers major benefits in fuel efficiency, payload capacity, and fatigue performance. Yet achieving reliable adhesive bonds on CFRP surfaces remains a persistent engineering challenge. The low intrinsic surface energy of composites - particularly under thermal cycling, vibration, and moisture exposure - limits bond durability unless surfaces are properly prepared. Plasma surface treatment has emerged as a pivotal solution, offering a fast, controllable, and non-destructive way to increase surface energy, improve wettability, and enhance adhesion across complex geometries. This is especially important as the aerospace industry transitions from thermoset to thermoplastic composites (TPCs), which enable faster processing, lower
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
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
The Mahindra XUV 3XO is a compact SUV, the first-generation of which was introduced in 2018. This paper explores some of the challenges entailed in developing the subsequent generation of this successful product, maintaining exterior design cues while at the same time improving its aerodynamic efficiency. A development approach is outlined that made use of both CFD simulation and Coastdown testing at MSPT (Mahindra SUV proving track). Drag coefficient improvement of 40 counts (1 count = 0.001 Cd) can be obtained for the best vehicle exterior configuration by paying particular attention to: AGS development to limit the drag due to cooling airflow into the engine compartment Front wheel deflector optimization Mid underbody cover development (beside the LH & RH side skirting) Wheel Rim optimization In this paper we have analyzed the impact of these design changes on the aerodynamic flow field, Pressure plots and consequently drag development over the vehicle length is highlighted. An
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