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In order to reduce engine out CO2 emissions it is a main subject to find new alternative fuels out of renewable sources. For this reason in this paper a blend out of 1-octanol and di-n-butylether and pure di-n-butylether are investigated in comparison to n-heptane as diesel-like fuel. The alternative fuels have a different combustion behavior particularly concerning important combustion parameters like ignition delay and mixture formation. Especially the formation of pollutants like nitrogen oxides in the combustion of alternative fuels is of global interest. The knowledge of the combustion behavior is important to design new engine geometries or implement a new calibration of the engine. In previous measurements in a single cylinder engine it was found out that both alternative fuels form nearly no soot emissions. For this reason now NOx is investigated optically to avoid the traditional soot NOx trade-off in diesel combustion.

Lignocellulosic biomass consists of (hemi-) cellulose and lignin. Accordingly, an integrated biorefinery will seek to valorize both streams into higher value fuels and chemicals. To this end, this study evaluated the overall combustion performance of both cellulose- and lignin derivatives, namely the high cetane number (CN) di-n-butyl ether (DnBE) and low CN anisole, respectively. Said compounds were blended both separately and together with EN590 diesel. Experiments were conducted in a single cylinder compression ignition engine, which has been optimized for improved combustion characteristics with respect to low emission levels and at the same time high fuel efficiency. The selected operating conditions have been adopted from previous “Tailor-Made Fuels from Biomass (TMFB)” work.

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.

Powertrain electrification is becoming increasingly common in the transportation sector to address the challenges of global warming and deteriorating air quality. This paper introduces a novel “Build Your Hybrid” approach to experience and test various hybrid powertrain concepts. This approach is applied to the light commercial vehicles (LCV) segment due to the attractive combination of a Diesel engine and a partly electrified powertrain. For this purpose, a demonstrator vehicle has been set up with a flexible P02 hybrid topology and a prototype Hybrid Control Unit (HCU). Based on user input, the HCU software modifies the control functions and simulation models to emulate different sub-topologies and levels of hybridization in the demonstrator vehicle. Three powertrain concepts are considered for LCVs: HV P2, 48V P2 and 48V P0 hybrid. Dedicated hybrid control strategies are developed to take full advantage of the synergies of the electrical system and reduce CO2 and NOx emissions.

Faced with one of the greatest challenges of humanity – climate change – the European Union has set out a strategy to achieve climate neutrality by 2050 as part of the European Green Deal. To date, extensive research has been conducted on the CO2 life cycle analysis of mobile propulsion systems. However, achieving absolute net-zero CO2 emissions requires the adjustment of the relevant key performance indicators for the development of mobile propulsion systems. In this context, research is presented that examines the ecological and economic sustainability impacts of a hydrogen-fueled mild hybrid vehicle, a hydrogen-fueled 48V hybrid vehicle, a methanol-fueled 400V hybrid vehicle, a methanol-to-gasoline-fueled plug-in hybrid vehicle, a battery electric vehicle, and a fuel cell electric vehicle. For this purpose, a combined Life-Cycle Assessment (LCA) and Life-Cycle Cost Assessment was performed for the different propulsion concepts.

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.

Hybrid and electric vehicles present a promising trade-off between the necessary reductions in emissions and fuel consumption, the improvement in driving pleasure and performance of today's and tomorrow's vehicles. These hybrid vehicles rely primarily on electronics for the control and the coordination of the different sub-systems or components. The number and complexity of the functions distributed over many control units is increasing in these vehicles. Functional safety, defined as absence of unacceptable risk due to the hazards caused by mal-function in the electric or electronic systems is becoming a key factor in the development of modern vehicles such as electric and hybrid vehicles. This important increase in functional safety-related issues has raised the need for the automotive industry to develop its own functional safety standard, ISO 26262.