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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 course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. Production and continual improvement of safe and reliable products is key in the aviation, space, and defense industries. Customer and regulatory requirements must not only be met, but they are typically expected to exceeded requirements. Due to globalization, the supply chain of this industry has been expanded to countries which were not part of it in the past and has complicated the achievement of requirements compliance and customer satisfaction.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. In the Aerospace Industry there is a focus on Defect Prevention to ensure that quality goals are met. Failure Mode and Effects Analysis (PFMEA) and Control Plan activities are recognized as being one of the most effective, on the journey to Zero Defects. This two-day course is designed to explain the core tools of Design Failure Mode and Effects Analysis (DFMEA), Process Flow Diagrams, Process Failure Mode and Effects Analysis (PFMEA) and Control Plans as described in AS13100 and RM13004.

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.

In this paper we are presenting the concept of automated park and charge functions in different use scenarios. The main scenario is automated park and charge in production and the other use scenario is within automated valet parking in parking garages. The automated park and charge in production is developed within the scope of the publicly funded project E-Self. The central aim of the project is the development and integration of automated driving at the end-of-line in the production at Ford Motor Company's manufacturing plant in Cologne. The driving function thereby is mostly based upon automated valet driving with an infrastructure based perception and action planning. Especially for electric vehicles the state of charge of the battery is critical, since energy is needed for all testing and driving operations at end-of-line.

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.

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.