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Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

Oxygenated fuels such as polyoxymethylene dimethyl ethers (OME) offer a chance to significantly decrease emissions while switching to renewable fuels. However, compared to conventional diesel fuel, they have lower heating values and different evaporation behaviors which lead to differences in spray, mixture formation as well as ignition delay. In order to determine the mixture formation characteristics and the combustion behavior of neat OME3-5, optical investigations have been carried out in a high-pressure-chamber using shadowgraphy, mie-scatterlight and OH-radiation recordings. Liquid penetration length, gaseous penetration length, lift off length, spray cone angle and ignition delay have been determined and compared to those measured with diesel-fuel over a variety of pressures, temperatures, rail pressures and injection durations.

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.