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A model based on an ionization equilibrium analysis, that can relate the ion current to the state of the gas inside the combustion volume, has been presented earlier. This paper introduces several additional models, that together with the previous model have the purpose of improving the pressure predictions. One of the models is a chemistry model that enables us to realistically consider the current contribution from the most relevant species. A second model can predict the crank angle of the peak pressure and thereby substantially increase the accuracy of the pressure predictions. Several other additions and improvements have been introduced, including support for part load engine conditions.

One way to extend the lean burn limit of a natural gas engine is by addition of hydrogen to the primary fuel. This paper presents measurements made on a one cylinder 1.6 liter natural gas engine. Two combustion chambers, one slow and one fast burning, were tested with various amounts of hydrogen (0, 5, 10 and 15 %-vol) added to natural gas. Three operating points were investigated for each combustion chamber and each hydrogen content level; idle, part load (5 bar IMEP) and 13 bar IMEP (simulated turbocharging). Air/fuel ratio was varied between stoichiometric and the lean limit. For each operating point, a range of ignition timings were tested to find maximum brake torque (MBT) and/or knock. Heat-release rate calculations were made in order to assess the influence of hydrogen addition on burn rate. Addition of hydrogen showed an increase in burn rate for both combustion chambers, resulting in more stable combustion close to the lean limit.

Test of autonomous systems is mostly brute force and ad-hoc thus being neither efficient nor transparent. Though requirements invite for a situational transparency, a framework is missing to judge quality of requirements and derived test-cases. Practical challenges are state explosion, difficulty to derive corner cases, no systematic safety of the intended functionality as specified, lack of accepted KPI, etc. Maintaining a valid safety case is hardly possible with such adaptive systems and continuous software updates. To achieve trusted autonomous vehicles, test cases must be generated automatically while at same time providing coverage (e.g., indicating progress with KPI), efficiency (e.g., limiting the amount of regression testing) and transparency (e.g., showing how specific corner cases are tested in case of accidents). This paper provides a method for automatically generating test cases for AI-based autonomous systems and compares it with existing testing methods.

In recent years, driving simulators have become a valuable tool in the automotive design and testing process. Yet, in the field of vehicle dynamics, most decisions are still based on test drives in real cars. One reason for this situation can be found in the fact that many driving simulators do not allow the driver to evaluate the handling qualities of a simulated vehicle. In a driving simulator, the motion cueing algorithm tries to represent the vehicle motion within the constrained motion envelope of the motion platform. By nature, this process leads to so called false cues where the motion of the platform is not in phase or moving in a different direction with respect to the vehicle motion. In a driving simulator with classical filter-based motion cueing, false cues make it considerably more difficult for the driver to rate vehicle dynamics.

In order to achieve high customer satisfaction and to avoid high warranty costs caused by component failures of the power-train of hybrid electric vehicles (HEV), car manufacturers have to optimize the dimensioning of these elements. Hence, it is obligatory for them to gain knowledge about the different types of vehicle usage being predominant all over the world. Therefore, in this paper we present a Data Mining system that employs a Random Forest (RF) based dissimilarity measure in the dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) to automatically identify and visualize different types of vehicle usage by applying these methods to aggregated logged on-board data, i.e., load spectrum data. This kind of data is calculated and recorded directly on the control units of the vehicles and consists of aggregated numerical data, like the histogram of the velocity signal or the traveled distance of a vehicle.

The development and qualification of distributed and highly safety-critical avionics systems implicate high efforts and risks. The resulting costs usually limit implementations like fly-by-wire systems to the military or commercial airliner domains. The aim of previous and ongoing research at the Institute of Aircraft Systems at University of Stuttgart is the reduction of these costs and therefore open up their benefits, inter alia, to general aviation, remotely piloted or unmanned aircraft. An approach for an efficient development is the application of a platform based development which supports the reuse of software and hardware components. The Flexible Platform adopts this approach. It is accompanied by a tool suite which automates the design and parameter instantiation, documentation generation and the generation of verification artifacts for a platform instance. This paper presents the approach for the requirement document generation compliant to ARP4754A and DO-178C.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

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.