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Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.

The pace of innovations in battery development is revolutionizing the landscape and opportunities for energy storage applications leading to a stronger market segmentation enabling a better suitability to fulfill specific application requirements. For automotive applications, several approaches to increase energy densities, to improve fast charging performance, and to reduce cost on a pack level are considered. Among them, a promising example is the direct integration of battery cells into the battery pack (Cell-to-pack; CTP) or vehicle (Cell-to-chassis, CTC) to increase energy densities and to reduce costs, as already commercialized by Tesla, CATL and others. In the pack development, especially Asian players are one of the frontrunners, where e.g., hybrid cell battery systems with a mixture of cells with different cathode chemistries as introduced by NIO, are experiencing a high interest of the market.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

Autonomous driving is a hot topic in the automotive domain, and there is an increasing need to prove its reliability. They use machine learning techniques, which are themselves stochastic techniques based on some kind of statistical inference. The occurrence of incorrect decisions is part of this approach and often not directly related to correctable errors. The quality of the systems is indicated by statistical key figures such as accuracy and precision. Numerous driving tests and simulations in simulators are extensively used to provide evidence. However, the basis of all descriptive statistics is a random selection from a probability space. The difficulty in testing or constructing the training and test data set is that this probability space is usually not well defined. To systematically address this shortcoming, ontologies have been and are being developed to capture the various concepts and properties of the operational design domain.

The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

In pursuing sustainable automotive technologies, exploring alternative fuels for hybrid vehicles is crucial in reducing environmental impact and aligning with global carbon emission reduction goals. This work compares methanol and naphtha as potential suitable alternative fuels for running in a battery-driven light-duty hybrid vehicle by comparing their performance with the diesel baseline engine. This work employs a 0-D vehicle simulation model within the GT-Power suite to replicate vehicle dynamics under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The vehicle choice enables the assessment of a delivery application scenario using distinct payload capacities: 0%, 25%, 50%, and 100%. The model is fed with engine maps derived from previous experimental work conducted in the same engine, in which a full calibration was obtained that ensures the engine's operability in a wide region of rotational speed and loads.

Expansion chamber mufflers are commonly applied to reduce noise in HVAC. Dissipative materials, such as microperforated plates (MPPs), are often applied to achieve a more broadband mitigation effect. Such mufflers are typically characterized in the frequency domain, assuming time-harmonic excitation. From a computational point of view, transient analyses are more challenging. A transformation of the equivalent fluid model or impedance boundary conditions into the time domain induces convolution integrals. We apply the recently proposed finite element formulation of a time domain equivalent fluid (TDEF) model to simulate the transient response of dissipative acoustic media to arbitrary unsteady excitation. As most time domain approaches, the formulation relies on approximating the frequency-dependent equivalent fluid parameters by a sum of rational functions composed of real-valued or complex-conjugated poles.

This work aims at investigating the optimal configuration of an internal combustion engine fueled with bio-ethanol for improving its brake power and efficiency as well as for reducing the NOx emissions, in stationary applications. A turbocharged spark ignition engine characterized by a single-point injection was preliminarily considered; subsequently, a direct injection configuration was investigated. For both cases, a 1-D numerical model was developed to compare the injection configurations under stoichiometric conditions and different spark timings. The analysis shows that the direct injection guarantees: a limited improvement of brake power and efficiency when the same spark timing is adopted, while NOx emissions increases by 20%; an increase of 6% in brake power and 2 percentage points in brake thermal efficiency by adopting the knock limited spark advance, but an almost double NOx emissions increase.

This study explores the feasibility of using a sustainable lignin-based fuel, consisting of 44 % lignin, 50 % ethanol, and 6 % water, in conventional compression ignition (CI) marine engines. Through experimental evaluations on a modified small-bore CI engine, we identified the primary challenges associated with lignin-based fuel, including engine startup and shutdown issues due to solvent evaporation and lignin solidification inside the fuel system, and deposit formation on cylinder walls leading to piston ring seizure. To address these issues, we developed a fuel switching system transitioning from lignin-based fuel to cleaning fuel with 85 vol% of acetone, 10 vol% of water and 5 vol% of ignition improving additive, effectively preventing system clogs.

Nowadays, the push for more ecological low-carbon propulsion systems is high in all mobility sectors, including the recreational or light-commercial boating, where propulsion is usually provided by internal combustion engines derived from road applications. In this work, the effects of replacing conventional fossil-derived B7 diesel with Hydrotreated Vegetable Oil (HVO) were experimentally investigated in a modern Medium-Duty Engine, using the advanced biofuel as drop-in and testing according to the ISO 8178 marine standard. The compounded results showed significant benefits in terms of NOx, Soot, mass fuel consumption and WTW CO2 thanks to the inner properties of the aromatic-free, hydrogen-rich renewable fuel, with no impact on the engine power and minimal deterioration of the volumetric fuel economy.

Reductions in powertrain noise have led to an increased proportion of road noise, prompting various studies aimed at mitigating it. Road excitation primarily traverses through the vehicle suspension system, necessitating careful optimization of the characteristics of bushings at connection points. However, optimizing at the vehicle assembly stage is both time-consuming and costly. Therefore, it is essential to proceed with optimization at the subsystem level using appropriate objective functions. In this study, the blocked force and energy-based index derived from complex power were used to optimize the NVH performance. Calculating the complex power in each bushing enables computing the power flow, thereby providing a basis for evaluating the NVH performance. Through stiffness injection, the frequency response functions (FRF) of the system can be predicted according to arbitrary changes in the bushing stiffness.

Gas bearings are an effective solution to high-speed rotor applications for its contamination free, reduced maintenance and higher reliability. However, low viscosity of gas leads to lower dynamic stiffness and damping characteristics resulting in low load carrying capacity and instability at higher speeds. Gas bearings can be enhanced by adding a foil structure commonly known as gas foil bearings (GFBs), whose dynamic stiffness can be tailored by modifying the geometry and the material properties resulting in better stability and higher load carrying capacity. A detailed study is required to assess the performance of high-speed rotor systems supported on GFBs, therefore in this study a bump type GFB is analyzed for its static and dynamic characteristics. The static characteristics are obtained by solving the non-linear Reynolds equation through an iterative procedure.

Many research centers and companies in general aviation have been devoting efforts to the electrification of propulsive plants to reduce environmental impact and/or increase safety. Even if the final goal is the elimination of fossil fuels, the limitations of today's battery in terms of energy and power densities suggest the adoption of hybrid-electric solutions that combine the advantages of conventional and electric propulsive systems, namely reduced fuel consumption, high peak power, and increased safety deriving from redundancy. Today, lithium batteries are the best commercial option for the electrification of all means of transportation. However, lithium batteries are a family of technologies that presents a variety of specifications in terms of gravimetric and volumetric energy density, discharge and charge currents, safety, and cost.

The benefits introduced by the replacement of conventional centrifugal pumps with volumetric machines for Internal Combustion Engines (ICEs) cooling were experimentally and theoretically proven in literature. In particular, Sliding Rotary Vane Pumps (SVRPs) ensure to achieve an interesting reduction of ICEs fuel consumption and CO2 emissions. Despite volumetric pumps are a reference technology for ICE lubrication oil circuits, the application in ICE cooling systems still not represent a ready-to-market solution. Particularly challenging is the case of Heavy-Duty ICE due to the wide operating range the pump covers in terms of flow rate delivered. Generally, SVRPs are designed to operate at high speeds to reduce machine dimensions and, consequently, the weight. Nevertheless, speed increase could lead to a severe penalization of pump performance since the growth of the friction losses.

Computer modelling, virtual prototyping and simulation is widely used in the automotive industry to optimize the development process. While the use of CAE is widespread, on its own it lacks the ability to provide observable acoustics or tactile vibrations for decision makers to assess, and hence optimize the customer experience. Subjective assessment using Driver-in-Loop simulators to experience data has been shown to improve the quality of vehicles and reduce development time and uncertainty. Efficient development processes require a seamless interface from detailed CAE simulation to subjective evaluations suitable for high level decision makers. In the context of perceived vehicle vibration, the need for a bridge between complex CAE data and realistic subjective evaluation of tactile response is most compelling. A suite of VI-grade noise and vibration simulators have been developed to meet this challenge.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

Airframe section of rockets, missiles and launch vehicles are typically cylindrical in shape. The cylindrical shell is subjected to high axial load and an external pressure during its operation. The design of cylinders subjected to such loads is generally found to be critical in buckling. To minimize the weight of cylinders, it is typically stiffened with rings and stringers on the inner diameter to increase the buckling load factor. Conventionally the buckling load estimated by analytical or numerical means is multiplied by an empirical factor generally called Knockdown factor (kdf) to get the critical buckling load. This factor is considered to account for the variation between theory and experiment and is specified by handbooks or codes. In aerospace industry, NASA SP 8007 is commonly followed and it specifies the kdf as a lower bound fit curve for experimental data .

Dimensional optimization has always been a time consuming process, especially for aerodynamic bodies, requiring much tuning of dimensions and testing for each sample. Aerodynamic auxiliaries, especially wings, are design dependent on the primary model attached, as they influence the amount of lift or reduction in drag which is beneficial to the model. In this study CFD analysis is performed to obtain pressure counter of wings. For a wing, the angle of attack is essential in creating proper splits to incoming winds, even under high velocities with larger distances from the separation point. In the case of a group of wings, each wing is then mentioned as a wing element, and each wing is strategically positioned behind the previous wing in terms of its vertical height and its self-angle of attack to create maximum lift. At the same time, its drag remains variable to its shape ultimately maximizing the C L /C D ratio.

The study of aerodynamic forces in hypersonic environments is important to ensure the safety and proper functioning of aerospace vehicles. These forces vary with the angle of attack (AOA) and there exists an optimum angle of attack where the ratio of the lift to drag force is maximum. In this paper, computational analysis has been performed on a blunt cone model to study the aerodynamic characteristics when hypersonic flow is allowed to pass through the model. The flow has a Mach number of 8.44 and the angle of attack is varied from 0º to 20º. The commercial CFD solver ANSYS FLUENT is used for the computational analysis and the mesh is generated using the ICEM CFD module of ANSYS. Air is selected as the working fluid. The simulation is carried out for a time duration of 1.2 ms where it reaches a steady state and the lift and drag forces and coefficients are estimated. The pressure, temperature, and velocity contours at different angles of attack are also observed.