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Abstract. With the COP28 decisions the world is thriving for a future net-zero-CO2 society and the and current regulation acts, the energy infrastructure is changing in direction of renewables in energy production. All industry sectors will extend their share of direct or indirect electrification. The question might arise if the build-up of the renewables in energy production is fast enough. Demand and supply might not match in the short- and mid-term. The paper will discuss the roadmaps, directions and legislative boundary parameter in the regenerative energy landscape and their regional differences. National funding on renewables will gain an increasing importance to accelerate the energy transformation. The are often competing in attracting the same know-how on a global scale. In addition the paper includes details about energy conversion, efficiency as well as potential transport scenarios from production to the end consumer.

The advent of digitalization opens up new avenues for advances in large internal combustion engine technology. Key engine components are becoming "intelligent" through advanced instrumentation and data analytics. By generating value-added data, they provide deeper insight into processes related to the components. An intelligent common rail diesel fuel injection valve for large engine applications in combination with machine learning allows reliable prediction of key combustion parameters such as maximum cylinder pressure, combustion phasing and indicated mean effective pressure. However, fault-related changes to the injection valve also have to be considered. Based on experiments on a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 liter, this study investigates the extent to which the intelligent injection valve can improve the reliability of combustion parameter predictions in the presence of injection valve faults.

The parameterization of the electrochemical pseudo-two-dimensional (P2D) model plays an important role as it determines the acceptance and application range of subsequent simulation studies. Electrochemical impedance spectroscopy (EIS) is commonly applied to characterize batteries and to obtain the exchange current density and the solid diffusion coefficient of a given electrode material. EIS measurements performed with frequencies ranging from 1 MHz down to 10 mHz typically do not cover clearly isolated solid state diffusion processes of lithium ions in positive or negative electrode materials. To extend the frequency range down to 10 μHz, the distribution function of relaxation times (DRT) is a promising analysis method. It can be applied to time-domain measurements where the battery is excited by a current pulse and relaxed for a certain period.

Modeling of lithium iron phosphate electrodes calls for appropriate extensions of established model approaches. An electrochemical pseudo two-dimensional and a single-particle model are enhanced to address the phase separating behavior of this material with a variable solid state diffusion model. A particle size distribution model tackles the heterogeneity of the electrode microstructure. Both models are embedded in a framework to describe multi-layer electrode designs featuring segregated material properties. The models are parameterized following literature replicating a good match with measured discharge curves at low, medium and high currents. A simplified version of the rigorous model shows the effort of reparameterization, the computational advantage of model order reduction techniques, the model accuracy and application scope.

Upcoming, increasingly stringent greenhouse gas (GHG) as well as emission limits demand for powertrain electrification throughout all vehicle applications. Increasing complexity of electrified powertrain architectures require an overall system approach combining modular component technology with integration and industrialization requirements when heading for further significant efficiency optimization. At the same time focus on reduced development time, product cost and minimized additional investment demand reuse of current production, machining, and assembly facilities as far as possible. Up to date additive manufacturing (AM) is an established prototype component, as well as tooling technology in the powertrain development process, accelerating procurement time and cost, as well as allowing to validate a significantly increased number of variants. The production applications of optimized, dedicated AM-based component design however are still limited.

The transition towards battery electric vehicles (BEVs) has increased the focus of vehicle manufacturers on energy efficiency. Ensuring adequate airflow through the heat exchanger is necessary to climatize the vehicle, at the cost of an increase in the aerodynamic drag. With lower cooling airflow requirements in BEVs during driving, the front air intakes could be made smaller and thus be placed with greater freedom. This paper explores the effects on exterior aerodynamics caused by securing a constant cooling airflow through intakes at various positions across the front of the vehicle. High-fidelity simulations were performed on a variation of the open-source AeroSUV model that is more representative of a BEV configuration. To focus on the exterior aerodynamic changes, and under the assumption that the cooling requirements would remain the same for a given driving condition, a constant mass flow boundary condition was defined at the cooling airflow inlets and outlets.

The internal combustion engine contains several actuators to control engine performance and emissions. These are controlled within the engine ECU and follow a specific operating strategy to achieve objectives such as NOx reduction and fuel economy. However, these two goals are conflicting and a compromise is required. The operating state depends on system constraints such as engine speed, load, temperature levels, and aftertreatment system efficiency. This results in constantly changing target values to stay within the defined limits, especially the legal emission limits. The conventional approach is to use multiple operating modes. Each mode represents a specific compromise and is activated accordingly. Multiple modes are required to meet emissions regulations under all required conditions, which increases the calibration effort. This new control approach uses an artificial neural network to replace the conventional multiple mode approach.

This work elaborates the transferability of electrode diffusion coefficients gained from fitting procedures in frequency domain to an electrochemical battery model run in time domain. An electrochemical battery model of an NMC622 half-cell electrode is simulated with sinusoidal current excitations at different frequencies. The current and voltage signals are analyzed in frequency domain via Nyquist and Bode plots. The frequency domain analysis of time domain simulations is applied to assess the numerical convergence of the simulation and the sensitivity on particle diameter, electrode and electrolyte diffusion coefficients. The simulated frequency spectra are used to fit the electrode diffusion coefficient by means of different electrical equivalent circuit models and the electrochemical battery model itself. The fitted diffusion coefficients from the different electrical equivalent circuit models deviate by one order of magnitude from the a priori known reference data.

The automotive industry continues to increase the utilization of computer-aided engineering. This put demands on finding reliable objective measures that correlate to subjective driver assessments on driving stability performance. However, the drivers’ subjective perception of driving stability can be difficult to quantify objectively, especially on test tracks where the wind conditions cannot be controlled. The advancement in driving simulator technology may enable evaluation of driving stability with high repeatability. The purpose of this study is to correlate the subjective assessment of driving stability to reliable objective measures and to evaluate the usefulness of a driving simulator for the subjective assessment. Two different driver clinic studies were performed in a state-of-the-art driving simulator. The first study included 38 drivers (professional, experienced and common drivers) and focused on crosswind gust sensitivity.

Heavy-duty vehicles are primarily powered by diesel fuel, emitting CO2 emissions regardless of the exhaust after-treatment system. Contrastingly, a hydrogen engine has the potential to decarbonize the transportation sector as hydrogen is a carbon free, renewable fuel. In this study, a multi-physics 1D simulation tool (GT-Power) is used to model the gas exchange process and performance prediction of a two-stroke hydrogen engine. The aim is to establish a maximum torque-level for a four-stroke hydrogen engine and then utilize different methods for two-stroke modeling to achieve similar torque by optimizing the gas exchange process. A camless engine is used as base, enabling the flexibility to utilize approximately square valve lift profiles. The preliminary step is the GT-Power model validation, which has been done using diesel and hydrogen engines (single-cylinder heavy-duty) experiments at different operating points (871 rpm, 1200 rpm, 1259 rpm, and 1508 rpm).

Increasingly stringent greenhouse gas and emission limits demand for powertrain electrification throughout all vehicle applications. Beside fully electric powertrains different configurations of hybrid powertrains will have an important role in upcoming and future vehicle generations. As already discussed in previous papers, the requirements on the combustion engine in hybrid powertrains are different to those in a conventional powertrain solution, heading for brake thermal efficiency targets of 45% and above within the product lifecycle for conventional fuels. Focus on product cost and production and assembly facility investment drives reuse of technology packages within modular powertrain technology platforms, with different combinations of internal combustion engines (ICE), transmissions, and e-drive-layouts. The goal of zero carbon operation requires compatibility of ICE for sustainable fuels.

In vehicle development, a subjective evaluation of the vehicle’s behavior at high speeds is usually conducted by experienced drivers with the objective of assessing driving stability. To avoid late design changes, it is desirable to predict and resolve perceived instabilities early in the development phase. In this study, a mathematical model is developed from measurements during on-road tests to predict the driver’s ability to identify vehicle instabilities under excitations such as aerodynamic excitations. A vehicle is fitted with add-ons to create aerodynamic excitations and is driven by multiple drivers on a high-speed track. Drivers’ evaluation, responses, cabin motion, and crosswind conditions are recorded. The influence of yaw and roll rates, lateral acceleration, and steering angle at various frequency ranges when predicting the drivers’ evaluation of induced excitation is demonstrated. The drivers’ evaluation of vehicle behavior is influenced by driver-vehicle interactions.

The shift toward electrification and limitations in battery electric vehicle technology have led to high demand for hybrid vehicles (HEVs) that utilize a battery and an internal combustion engine (ICE) for propulsion. Although HEVs enable lower fuel consumption and emissions compared to conventional vehicles, they still require combustion of fuels for ICE operation. Thus, emissions from hybrid vehicles are still a major concern. Engine starts are a major source of emissions during any driving event, especially before the three-way catalyst (TWC) reaches its light-off temperature. Since the engine is subjected to multiple starts during most driving events, it is important to mitigate and better understand the impact of these emissions. In this study, experiments were conducted to analyze engine starts in a hybrid powertrain on different experimental setup.

Soot particle size, particle concentration and volume fraction were measured by laser based methods in optically dense, highly turbulent combusting diesel sprays under engine-like conditions. Experiments were done in the Chalmers High Pressure, High Temperature spray rig under isobaric conditions and combusting commercial diesel fuel. Laser Induced Incandescence (LII), Elastic Scattering and Light Extinction were combined quasi-simultaneously to quantify particle characteristics spatially resolved in the middle plane of a combusting spray at two instants after the start of combustion. The influence that fuel injection pressure, gas temperature and gas pressure exert on particle size, particle concentration and volume fraction were studied. Probability density functions of particle size and two-dimensional images of particle diameter, particle concentration and volume fraction concerning instantaneous single-shot cases and average measurements are presented.

Pre-chamber combustion (PCC) has re-emerged in recent last years as a potential solution to help to decarbonize the transport sector with its improved engine efficiency as well as providing lower emissions. Research into the combustion process inside the pre-chamber is still a challenge due to the high pressure and temperatures, the geometrical restrictions, and the short combustion durations. Some fundamental studies in constant volume combustion chambers (CVCC) at low and medium working pressures have shown the complexity of the process and the influence of high pressures on the turbulence levels. In this study, the pre-chamber combustion process was investigated by combustion visualization in an optically-accessible pre-chamber under engine relevant conditions and linked with the jet emergence inside the main chamber. The pre-chamber geometry has a narrow-throat. The total nozzle area is distributed in two six-hole rows of nozzle holes.

Upcoming, increasingly stringent greenhouse gas (GHG) as well as emission limits demand for powertrain electrification throughout all vehicle applications. Increasing complexity of electrified powertrain architectures require an overall system approach combining component technology with integration and industrialization requirements when heading for further significant efficiency optimization of the subsystem internal combustion engine. The requirements on the combustion engine in hybrid powertrains are quite different to those in a conventional powertrain solution. Next-generation hybrid engines, with brake thermal efficiency (BTE) targets starting from 42-43% and aiming for 45% and above within the product lifecycle, require a re-thinking of the base engine architecture of current modular engine platforms. At the same time focus on the product cost and minimized additional investment demand reuse of current production, machining and assembly facilities as far as possible.

Fast charging of batteries at cold conditions faces the challenge of promoting undesired cell degradation phenomena such as lithium plating. The occurrence of lithium plating is strongly related to local surface potentials and temperatures involving the scales of the electrode surface, the unit cell and the entire module or pack. A multi-scale, multi-domain model is presented, enhancing a Newman based unit cell model with consistent models for heat generation and lithium plating and integrating this 1D+1D approach into a thermal 3D model on module level. The basic equations are presented and three different plating models from literature are discussed. The thermal model is assessed in open-loop simulations and the different plating approaches are compared in charge/discharge simulations at different operating conditions. The full multi-scale, multi-domain model is applied as a virtual sensor for model-based control of fast charging at cold conditions.

Today’s automotive industry is changing rapidly towards environmentally friendly vehicle propulsion systems. All over the globe, legislative CO2 consumption targets are under discussion and partly already in force. Hybrid powertrain configurations are capable to lower fuel consumption and limit pollutant emissions compared to pure IC-Engine driven powertrains. Depending on boundary conditions a numerous of different hybrid topologies- and its control strategies are thinkable. Typical approach is to find the optimum hybrid layout and strategy, by performing certain technical design tasks in office simulation directly followed by vehicle prototype tests on the chassis dyno and road. This leads to a high number of prototype vehicles, overload on chassis dynos, time consuming road test and finally to tremendous costs. Our developed approach is using the engine testbed with simulation capabilities as bridging element between office and vehicle development environment.

Alcohol-based fuels are a viable alternative to fossil fuels for powering vehicles. As a drop-in fuel, an oxygenated fuel blend containing the C8 alcohol 2-ethylhexanol (isomer of octanol), hydrotreated vegetable oil (HVO) and rapeseed methyl ester (RME) can reduce soot and NOx emissions whilst maintaining engine performance. However, fuel injection strategy significantly affects combustion and hence has been investigated with a view to reducing emissions whilst maintaining engine efficiency. In a single cylinder light-duty compression ignition research engine, the effect of different injection strategies (main, main/post, double pre/main, double pre/main/post injection) and EGR levels (0%, 19%) on specifically NOx, soot emissions and particle size distribution was investigated for three different fuels: fossil diesel fuel, HVO and the oxygenated blend. The blend was designed to have diesel-like combustion properties (cetane number of 52) and had an oxygen content of 5.4% by mass.

Approaching engineering limits for the thermal design of battery modules requires virtual prototyping and appropriate models with respect to physical depth and computational effort. A multi-scale and multi-domain model describes the electrochemical behavior of a single battery unit cell in 1D+1D at the level of intra-cell phenomena, and it applies a 3D thermal model at module level. Both models are connected within a common vehicle simulation platform. The models are discussed with special emphasis on battery degradation such as solid electrolyte interphase layer formation, decomposition and lithium plating. The performance of the electrochemical model is assessed by discharge cycles and repeated charge/discharge simulations. The thermal module model is compared to CFD reference data and studied with respect to its grid sensitivity.