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The 2-day foundational-level Fundamentals of GD&T course teaches the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2018 Standard. The class offers an explanation of geometric tolerances, including their symbols, tolerance zones, applicable modifiers, common applications, and limitations. It explains Rules #1 and #2, the datum system, form and orientation controls, tolerance of position (RFS and MMC), runout, and profile controls. Newly acquired learning is reinforced throughout the class with more than 130 practice exercises, including more than 60 application problems. 

The 2-day foundational-level Fundamentals of GD&T course teaches the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2009 Standard. The class offers an explanation of geometric tolerances, their symbols, tolerance zones, applicable modifiers, common applications, and limitations. It explains Rules #1 and #2, form and orientation controls, the datum system, tolerance of position (RFS and MMC), runout, and profile controls. Newly acquired learning is reinforced throughout the class with more than 80 practice exercises. 

Electric and hybrid vehicle engineers and designers are faced with the important issue of how to adequately configure required powertrain system components to achieve needed performance, occupant accommodation, and operational objectives. This course enables participants to fully comprehend vehicle architectural/configurational design requirements to enable efficient structural design, effective packaging of required components, and efficient vehicle performance for shared and autonomous operation. The importance of integrating these design requirements with specific vehicle user needs and expectations will be emphasized.

This is a three-day course which provides a comprehensive and up to date introduction to fuel cells for use in automotive engineering applications. It is intended for engineers and particularly engineering managers who want to jump‐start their understanding of this emerging technology and to enable them to engage in its development. Following a brief description of fuel cells and how they work, how they integrate and add value, and how hydrogen is produced, stored and distributed, the course will provide the status of the technology from fundamentals through to practical implementation.

In the context of urban smart mobility, vehicles have to communicate with each other, surrounding infrastructure, and other traffic participants. By using Vehicle2X communication, it is possible to exchange the vehicles’ position, driving dynamics data, or driving intention. This concept yields the use for cooperative driving in urban environments. Based on current V2X-communication standards, a methodology for cooperative driving of automated vehicles in mixed traffic scenarios is presented. Initially, all communication participants communicate their dynamic data and planned trajectory, based on which a prioritization is calculated. Therefore, a decentralized cooperation algorithm is introduced. The approach is that every traffic scenario is translatable to a directed graph, based in which a solution for the cooperation problem is computed via an optimization algorithm.

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 mobility industry and the entire ecosystem is currently striving towards sus-tainable mobility which leads to continuous production ramp-up of electrified vehicles. The parallel increase of the charging infrastructure is faced with various challenges regarding needed investments and the connection into the electricity grid. MAHLE chargeBIG offers centralized and large scaled charging infrastruc-ture with more than 1,800 already installed charging points. This presentation and paper is evaluating the functionality of the system by ana-lyzing backend real data of various employer parking installations. It can be shown and proven that a single-phase charging concept is sufficient and able to manage most customer relevant charging events by considering the needs and limitations of the related electricity grid infrastructure. Smart charging algorithms enable the integration of the charging infrastructure in smart grid company environments.

In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.

Society is moving towards climate neutrality where hydrogen fuelled combustion engines (H2 ICE) could be considered a main technology. These engines run on hydrogen (H2) so carbon-based emission are only present at a very low level from the lube oil. The most important pollutants NO and NO2 are caused by the exhaust aftertreatment system as well as CO2 coming from the ambient air. For standard measurement technologies these low levels of CO2 are hard to detect due to the high water content. Normal levels of CO2 are between 400-500 ppm which is very close or even below the detection limit of commonly used non-dispersive-infrared-detectors (NDIR). As well the high water content is very challenging for NOx measuring devices, like chemiluminescence detectors (CLD), where it results in higher noise and therefore a worse detection limit. Even for Fourier-transformed-infrared-spectroscopy-analysers (FT-IR) it is challenging to deal with water content over 15% without increased noise.

Autonomous Driving is being utilized in various settings, including indoor areas such as industrial halls. Additionally, LIDAR sensors are currently popular due to their superior spatial resolution and accuracy compared to RADAR, as well as their robustness to varying lighting conditions compared to cameras. They enable precise and real-time perception of the surrounding environment. Several datasets for on-road scenarios such as KITTI or Waymo are publicly available. However, there is a notable lack of open-source datasets specifically designed for industrial hall scenarios, particularly for 3D LIDAR data. Furthermore, for industrial areas where vehicle platforms with omnidirectional drive are often used, 360° FOV LIDAR sensors are necessary to monitor all critical objects. Although high-resolution sensors would be optimal, mechanical LIDAR sensors with 360° FOV exhibit a significant price increase with increasing resolution.