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Increasing urbanisation and the growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry standard load cases for the strength design of the components. To design the chassis components, first the available installation space is determined and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify component geometries that are suitable for the force flow. The optimisation setup is selected in a way that allows to integrate information, energy and material-carrying conductors into the suspension arms. The conductors even serve as load-bearing structures because of the matching design of the components.

The courier express parcel service industry (CEP industry) has experienced significant changes in the recent years due to increasing parcel volume. At the same time, the electrification of the vehicle fleets poses additional challenges. A major advantage of battery electric CEP vehicles compared to internal combustion engine vehicles is the ability to regenerate the kinetic energy of the vehicle in the frequent deceleration phases during parcel delivery. If the battery is cold the maximum recuperation power of the powertrain is limited by a reduced chemical reaction rate inside the battery. In general, the maximum charging power of the battery depends on the state of charge and the battery temperature. Due to the low power demand for driving during CEP operation, the battery self-heating is comparably low under cold ambient conditions. Without active conditioning of the battery, potential regenerative energy is lost as a result of the cold battery.

In the course of the U-Shift project, an automated, driverless and electrically driven vehicle concept is developed. By separating the vehicle into a drive module and a transport capsule, a novel form of mobility is created. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and thus serves both passenger and goods transport. In order to be able to use the vehicle effectively, especially in urban areas, the space required for manoeuvring and loading or unloading the capsules must be kept as small as possible. This poses special challenges for the steering system. In this paper, a novel steering system is presented that enables both same-direction and opposite-direction wheel steering. First, the fundamental concept of the steering system is presented. After that, the design is explained and the assembled steering system is shown. During normal cornering, there is a mechanical coupling between the wheels.

The automotive industry is continuously evolving, demanding innovative approaches to enhance testing methodologies and preventive identify potential issues. This paper proposes an advancement test approach in the area of the overall vehicle system included steering system and power train on a “Road to Rig” test bench. The research aims to revolutionize the conventional testing process by identifying faults at an early stage and eliminating the need to rely solely on field tests. The motivation behind this research is to optimize the test bench setup and bring it even closer to real field tests. Key highlights of the publication include the introduction of an expanded load spectrum, incorporating both steering angle and speed parameters along the test track. The load includes different route and driving profiles like on a freeway, overland and city drive in combination with the steering angles.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

Autonomous Emergency Braking (AEB) systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. Advancements in sensor technology and deep learning have improved vehicle perception and real-world understanding. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems.

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 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.

While conventional methods like classical Transfer Path Analysis (TPA), Multiple Coherence Analysis (MCA), Operational Deflection Shape (ODS), and Modal Analysis have been widely used for road noise reduction, component-TPA from Model Based System Engineering (MBSE) is gaining attention for its ability to efficiently develop complex mobility systems. In this research, we propose a method to achieve road noise targets in the early stage of vehicle development using component-level TPA based on the blocked force method. An important point is to ensure convergence of measured test results (e.g. sound pressure at driver ear) and simulation results from component TPA. To conduct component-TPA, it is essential to have an independent tire model consisting of tire blocked force and tire Frequency Response Function (FRF), as well as full vehicle FRF and vehicle hub FRF.

The NVH performance of electric vehicles is a key indicator of vehicle quality, being the structure-borne transmission predominating at low frequencies. Many issues are typically generated by high vibrations, transmitted through different paths, and then radiated acoustically into the cabin. A combined analysis, with both finite-element and multi-body models, enables to predict the interior vehicle noise and vibration earlier in the development phases, to reduce the development time and moreover to optimize components with an increased efficiency level. In the present work, a simulation of a Hyundai electric vehicle has been performed in IDIADA VPG with a full vehicle multibody (MBD) model, followed by vibration/acoustic simulations with a Finite elements model (FEM) in MSC. Nastran to analyze the comfort. Firstly, a full vehicle MBD model has been developed in MSC. ADAMS/Car including representative flexible bodies (generated from FEM part models).

By analyzing the dynamic distortion in all body closure openings in a complete vehicle, a better understanding of the body characteristics can be achieved compared to traditional static load cases such as static torsional body stiffness. This is particularly relevant for non-traditional vehicle layouts and electric vehicle architectures. The body response is measured with the so-called Multi Stethoscope (MSS) when driving a vehicle on a rough pavé road (cobble stone). The MSS is measuring the distortion in each opening in two diagonals. During the virtual development, the distortion is described by the relative displacement in diagonal direction in time domain using a modal transient analysis. The results are shown as Opening Distortion Fingerprint ODF and used as assessment criteria within Solidity and Perceived Quality. By applying the Principal Component Analysis (PCA) on the time history of the distortion, a Dominant Distortion Pattern (DDP) can be identified.

Meta material has been known for many years and the physics are well known since decades. But the challenge has always been to put the know how into (mass) production. This was the reason why no meta material has found its way into the automotive industry so far. But now things have changed: meta material became Meta Design and is going into serial production in 2024. Meta Design is a tunable spring mass system with foam acting as the spring and heavy layer as the mass. Meta Design is characterized by cavities in the foam and concentrated masses of the heavy layer as functionalized mass pins. By tuning the size of the cavities and the weight of the mass pins the acoustic performance can be adjusted to the requirements of each individual car line. After preliminary simulations, flat samples were tested in the lab. The next step was launched: the production and testing of a handmade prototype part of a firewall insulation for a Mercedes-Benz A-Class.

The current work presents the results of an investigation on the impact of renewable fuels on the combustion and emissions of a turbocharged compression-ignition internal combustion engine. An experimental study was undertaken and the engine settings were not modified to account for the fuel's chemical and physical properties, to analyze the performance of the fuel as a potential drop-in alternative fuel. Three fuels were tested: mineral diesel, a blend of it with waste cooking oil biodiesel and a hydrogenated diesel. The analysis of the emissions at engine exhaust highlights that hydrogenated fuel allows to reduce CO, total hydrocarbon emissions, particulate matter and NOx.

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.

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.

Nowadays, electrification is largely acknowledged as a crucial strategy to mitigate climate change, especially for the transportation sector through the transition from conventional vehicles to electric vehicles (EVs). As the demand for EVs continues to rise, the development of a robust and widespread charging infrastructure has become a top priority for governments and decision-makers. In this context, innovative approaches to energy management and sustainability, such as Vehicle-to-Grid (V2G), are gradually being employed, leading to new challenges, like grid service integration, charge scheduling and public acceptance. For instance, the planned use scenario, the user’s behaviour, and the reachability of the geographical position influence the optimal energy management strategies both maintain user satisfaction and optimize grid impact.

This research aims presents the method classifying the noise source and evaluating the sound quality of the noise caused by operating of electric power steering wheel in an electric vehicle. The steering wheel has been operated by the motor drive by electric power and it called motor-driven electric power (MDPS) system. If the motor is attached to the steering column of the steering device, it is called C-MDPS system. The steering device of the C-MDPS system comprises of motor, bearings, steering column, steering wheel and worm shaft. Among these components the motor and bearings are main noise sources of C-MDPS system. When the steering wheel is operated in an electric vehicle, the operating noise of the steering device inside the vehicle is more annoying than that in a gasoline engine vehicle since the operating noise is not masked by engine noise. Defects in the C-MDPS system worsen the operating noise of the steering system.

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.

The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, air temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin.