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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.

Increasingly stringent regulations relating to the emissions of passenger cars and commercial vehicles demand alternative powertrain technologies in order to effectively achieve the climate targets. Hydrogen can play a crucial role as alternative energy carrier regarding the EU targets for CO2-neutral mobility towards 2050. Therefore, it represents a reasonable choice not only for fuel cell powered vehicles, but also for fueling internal combustion engines (ICE). This paper focuses on the numerical investigation of high-pressure hydrogen injection and the mixture formation inside a high-tumble ICE with a conventional liquid fuel injector for passenger cars. Since the traditional 3D-CFD approach of simulating the inner flow of an injector requires a very high spatial and temporal resolution, the enormous computational effort, especially for full engine simulations, is a big challenge for an effective virtual development of modern ICEs.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

Opposed-piston two-stroke engines offer numerous advantages over conventional four-stroke engines, both in terms of fundamental principles and technical aspects. The reduced heat losses and large volume-to-surface area ratio inherently result in a high thermodynamic efficiency. Additionally, the mechanical design is simpler and requires fewer components compared to conventional four-stroke engines. When combining this engine concept with alternative fuels such as hydrogen and pre-chamber technology, a potential route for carbon-neutral powertrains is observed. To ensure safe engine operation using hydrogen as fuel, it is crucial to consider strict safety measures to prevent issues such as knock, pre-ignition, and backfiring. One potential solution to these challenges is the use of direct injection, which has the potential to improve engine efficiency and expand the range of load operation.