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The ongoing energy transition will have a profound impact on future mobility, with electrification playing a key role. Battery electric vehicles (EVs) are the dominant technology, relying on the conversion of alternating current (AC) from the grid to direct current (DC) to charge the traction battery. This process involves power electronic components such as rectifiers and DC/DC converters operating at high switching frequencies in the kHz range. Fast switching is essential to minimize losses and improve efficiency, but it might also generate electromagnetic interferences (EMI). Hence, electromagnetic compatibility (EMC) testing is essential to ensure reliable system operations and to meet international standards. During DC charging, the AC/DC conversion takes place off-board in the charging station, allowing for better cooling and larger components, resulting in increased power transfer, currently up to 350 kW.

The automotive PowerNet is facing a major transformation. The three main drivers are: • Increasing power • Availability requirements • PowerNet complexity and cost reduction These driving factors result in a wide variety of possible future PowerNet topologies. The increasing power demand is among others caused by the progressive electrification of formerly mechanical components and the trend of increasing number of comfort loads. This leads to a steady increase in installed electrical power. X-by-wire systems and autonomous driving functions result in higher availability requirements. As a result, the power supply of all safety-critical loads must always be kept sufficiently stable. To reduce costs and increase reliability, the car manufacturers aim to reduce the complexity of the PowerNet System, including the wiring harness and the controller network. The wiring harness e.g., is currently one of the costliest parts of modern cars. These challenges are met with different concepts.

When riding an e-bike, riders are faced with the question of whether there is enough energy left in the battery to reach the destination with the desired level of support. E-bike users therefore have an existential range anxiety. Specifically, this describes the fear that the battery charge will be exhausted before there is an opportunity to recharge it and that it will no longer be possible to use the electric support. However, e-bike riders have so far had to decide for themselves whether the available battery charge is sufficient for riding the planned route or whether the desired destination can be reached. In this context, the challenge is to decide how much support can be used so that an appropriate amount of effort can be achieved for the entire journey. In order to assist e-bike riders with this problem, the objective of this paper is to present an approach towards an assistance system that provides rider-adaptive support over the entire journey of a defined route.

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.

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.

Software will lead the development and life cycle of vehicles in the future. Nowadays, more and more software is being integrated into a vehicle, evolving it into a Software-Defined Vehicle (SDV). Automotive High Performance Computers (HPCs) serve as enablers by providing more computing infrastructure which can be flexibly used inside a vehicle. However, this leads to a complex vehicle system that needs to function today and in the future. Detecting and rectifying failures as quickly as possible is essential, but existing diagnostic approaches based on Diagnostic Trouble Codes (DTCs) are not designed for such complex systems and lack of flexibility. DTCs are predefined during vehicle development and changes to vehicle diagnostics require a large amount of modification work. Moreover, diagnostics are not intended to handle dynamically changing software systems and have shortcomings when applied to in-vehicle software 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 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.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

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.

The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

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.

The production of electric vehicles (EVs) has a significant environmental impact, with up to 50 % of their lifetime greenhouse gas potential attributed to manufacturing processes. The use of sustainable materials in EV design is therefore crucial for reducing their overall carbon footprint. Wood laminates have emerged as a promising alternative due to their renewable nature. Additionally, wood-based materials offer unique damping properties that can contribute to improved Noise, Vibration, and Harshness (NVH) characteristics. In comparison to conventional materials such as aluminum, ply wood structures exhibit beneficial damping properties. The loss factor of plywood structures with a thickness below 20 mm ranges from 0.013 to 0.032. Comparable aluminum structures however exhibit only a fraction of this loss factor with a range between 0.002 and 0.005.

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.

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.

A major issue of battery electric vehicles (BEV) is optimizing driving range and energy consumption. Under actual driving, transient thermal and electrical performance changes could deteriorate the battery cells and pack. These performances can be investigated and controlled efficiently with a thermal management system (TMS) via model-based development. A complete battery pack contains multiple cells, bricks, and modules with numerous coolant pipes and flow channels. However, such an early modeling stage requires detailed cell geometry and specifications to estimate the thermal and electrochemical energies of the cell, module, and pack. To capture the dynamic performance changes of the LIB pack under real driving cycles, the thermal energy flow between the pack and its TMS must be well predicted. This study presents a BTMS model development and validation method for a 75-kWh battery pack used in mass-production, mid-size battery SUV under WLTC.

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.

Though modal analysis is a common tool to evaluate the dynamic properties of a structure, there are still many individual decisions to be made during the process which are often based on experience and make it difficult for occasional users to gain reliable and correct results. One of those experience-based choices is the correct number and placement of reference points. This decision is especially important, because it must be made right in the beginning of the process and a wrong choice is only noticeable in the very end of the process. Picking the wrong reference points could result in incomplete modal analysis outcomes, as it might make certain modes undetectable, compounded by the user's lack of awareness about these missing modes. In the paper an innovative approach will be presented to choose the minimal number of mandatory reference points and their placement.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).