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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.

Expansion chamber mufflers are commonly applied to reduce noise in HVAC. Dissipative materials, such as microperforated plates (MPPs), are often applied to achieve a more broadband mitigation effect. Such mufflers are typically characterized in the frequency domain, assuming time-harmonic excitation. From a computational point of view, transient analyses are more challenging. A transformation of the equivalent fluid model or impedance boundary conditions into the time domain induces convolution integrals. We apply the recently proposed finite element formulation of a time domain equivalent fluid (TDEF) model to simulate the transient response of dissipative acoustic media to arbitrary unsteady excitation. As most time domain approaches, the formulation relies on approximating the frequency-dependent equivalent fluid parameters by a sum of rational functions composed of real-valued or complex-conjugated poles.

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.

India is the market with the highest sales of agricultural tractors and the market with the highest number of agricultural tractor park, as well. Even though taking into account the lower average power of Indian agricultural tractors compared to regions with considerably larger field sizes, their cumulated diesel fuel consumption reaches a significant size. The possible use of alternative powertrains like battery-electric, especially considering the lower power of the Indian tractor market, seems feasible, but might be struggling with challenges in terms of charging infrastructure and the possibly resulting lower productivity due to required charging times. Therefore AVL proposes to investigate the use of alternative fuels for internal combustion engines, a topic which is also being discussed by other global tractor OEMs. In that context the focus is typically on higher tractor powers due to current storage limitations of battery-electric systems and other alternatives.

Non-exhaust emissions are clearly one of the focal points for the upcoming Euro 7 legislation. The new United Nations Global Technical Regulation (UN GTR) defining the framework for brake emission measurements is about to be officially published. The first amendment to this text is already on the way through the United Nations Economic Commission for Europe (UNECE) hierarchy for decision making. In real life, the final emission factor as the ultimate result of a test is influenced by inaccuracies of numerous parts of the measurement system as well as additional contributing factors like the performance of the particulate filter handling process, which might not be primarily related to equipment specifications.

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.

Hydrogen promises to provide some highly desired features for clean and efficient combustion, but harvesting efficiency and emission potentials as well as meeting engine durability requirements needs careful adaption of both, combustion system components and engine operation strategies. Key points for H2-ICE combustion are some specific and unique features of H2/air mixtures, among which – to name only a few – excellent dilutability, lean burn capability, low ignition energy and high molecular diffusivity and their consequences on ICE operation do play prominent roles. H2 admission via port or direct injection, compression ratio selection and injection timing provide a set of parameters to control combustion features.

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.

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.

Fully flexible dual fuel (DF) internal combustion (IC) engines, that can burn diesel and gas simultaneously, have become established among heavy-duty engines as they contribute significantly to lower the environmental impact of the transport sector. In order to gain better understanding of the DF combustion process and establish an effective design methodology for DFIC engines, high fidelity computational fluid dynamics (CFD) simulation tools are needed. The DF strategy poses new challenges for numerical modelling of the combustion process since all combustion regimes have to be modelled simultaneously. Furthermore, DF engines exhibit higher cycle-to-cycle variations (CCV) compared to the pure diesel engines. This issue can be addressed by employing large eddy simulation coupled with appropriate DF detailed chemistry mechanism. However, such an approach is computationally too expensive for today’s industry-related engine calculations.

System lubrication in automotive powertrains is a growing topic for development engineers. Hybrid and pure combustion system complexity increases in search of improved efficiency and better control strategy, increasing the number of components with lubrication demand and the interplay between them, while fully electric systems drive for higher input speeds to increase e-motor efficiency, increasing bearing and gear feed rate demands. Added to this, many e-axle and hybrid systems are in development with a shared medium and circuit for e-motor cooling and transmission lubrication. Through all this, the lubricant forms a common thread and is a fundamental component in the system, but no standardized tests can provide a suitable methodology to investigate the adequate lubrication of components at powertrain level, to support the final planned vehicle usage.

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.

The demand for high efficiency powertrains in automotive engineering is further increasing, with hybrid powertrains being a feasible option to cope with new legislations. So far hybridization has only played a minor role for L-category vehicles. Focusing on an exemplary high-power L-category on-road vehicle, this research aims to show a new development approach, which combines longitudinal dynamic simulation (LDS) with “Design of Experiments” (DoE) in course of hybrid electric powertrain development. Furthermore, addressing the technological aspect, this paper points out how such a vehicle can benefit from 48V-hybridization of its already existing internal combustion powertrain. A fully parametric LDS model is built in Matlab/Simulink, with exchangeable powertrain components and an adaptable hybrid operation strategy. Beforehand, characterizing decisions as to focus on 48V and on parallel hybrid architecture are made.

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.

The further increase in the efficiency of heavy-duty engines is essential in order to reduce CO2 emissions in the transport sector. This is also valid for the future use of alternative fuels, which can be CO2-neutral, but can cause higher total costs of ownership due to higher prices and limited availability. In addition to thermodynamic optimization, the reduction of mechanical losses is of great importance. In particular, there is a high potential in the piston bore interface, since continuously increasing cylinder pressures have a strong influence on the frictional and lateral piston forces. To meet these future challenges of increasing heavy-duty engine efficiency, AVL has developed a floating liner engine for heavy-duty applications based on its tried and tested passenger car floating liner concept.

Global warming is the driver for introduction of CO2 and fuel consumption legislation worldwide. Indian truck manufacturers are facing the introduction of Indian fuel efficiency norms. In the European Union the CO2 emission monitoring phase of the most relevant truck classes was completed in June 2020 by usage of the Vehicle Energy Consumption Calculation TOol VECTO. Indian rule makers are currently considering an adaptation of VECTO for the usage in India, too. Indian truck market has always been very cost sensitive. Introduction of Bharat Stage VI Phase I has already led to a significant cost increase for emission compliance. Therefore, it will be of vital importance to keep the additional product costs for achievement of future fuel consumption legislation as low as possible as long as the real-world operation will not be promoted by the government.