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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.

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

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 importance of designing and sizing a thermal management system for electric vehicle powertrains cannot be overstated. Traditional approaches often rely on model-based system design using supplier reference component data, which can inadvertently lead to undisclosed errors arising from the interactions between the components and the environment. This paper introduces a novel test facility for battery electric vehicle thermal management technology, which has been designed for neural network virtual sensor and non-linear multi-in multi-out control development. The paper demonstrates how a digital twin of the test bench can used to support the development of such technology. Additionally, this paper presents preliminary results from the test bench revealing insights into the performance and interactions of key components. For instance, there is an observed 30% reduction in the maximum flow rate of the pump integrated into the test bench compared to the specified value.

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.

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.

Vibration testing is common in automotive industry validation and gains greater significance with increasing numbers of electrical components, which are particularly suspectable to vibration related failures. While the nature and intention of vibration testing is common, many contradicting testing standards claim to be a one-size-fits-all solution, leading to questions of which standard is correct for any specific application. This is compounded by the vast variation in vehicle types and applications (suspension systems, dampers, powertrain mass, tire radius, intended usage, etc.) This paper seeks to offer and demonstrate a method to determine characteristic vibration profiles, based on vehicle classes, and illuminate the process to accelerate these to an appropriate test profile. This can either be used to directly validate a system or to support the selection of the most appropriate vibration profile from options within standards.

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.

As electrified powertrains trends towards the new norm in development, the need to consider modular development approaches becomes more prevalent. Modular system developments seek to offer an adaptable product range by considering each system component (transmission, e-motor, inverter, battery, etc.) and system element (park-lock, disconnect, differential, etc.) as interchangeable. This can result in a lower cost development process overall to increase the returns for tier1 suppliers by expanding the marketability of the platform. Such an approach has hitherto held relatively low commercial interest as the rate of technological advancement negated the benefits of a modular development due to the lack of long-term competitivity. Previously large technological advances between successive productions and the relatively limited EV market, centred around SUV and small car applications, reduced the value in committing to a platform development.

Minimizing the lubricant volume in a transmission system reduces the churning losses and overall unit costs. However, lubricant volume reduction is also detrimental to the thermal stability of the system. Transmission overheating can result in significant issues in the region of loaded contacts, risking severe surface/sub-surface damage in bearings and gears, as well as reduction in the lubricant quality through advanced oxidation and shear degradation. The increasing trend of electrified transmission input speeds raises the importance of understanding the thermal limits of the system at the envelope of the performance to ensure quality and reliability can be maintained, as well as being a key factor in the development, effecting internal housing features for the promotion of lubrication. A nodal bearing thermal model will be shown which utilizes thermal resistances and smooth particle based CFD for determining bearing lubricant feed rates during operation.

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.

Hybrid technologies enable the reduction of noxious tailpipe emissions and conformance with ever-decreasing allowable homologation limits. The complexity of the hybrid powertrain technology leads to an energy management problem with multiple energy sinks and sources comprising the system resulting in a high-dimensional time dependent problem for which many solutions have been proposed. Methods that rely on accurate predictions of potential vehicle operations are demonstrably more optimal when compared to rule-based methodology [1]. In this paper, a previously proposed energy management strategy based on an offline optimization using dynamic programming is investigated. This is then coupled with an online model predictive control strategy to follow the predetermined optimal battery state of charge trajectory prescribed by the dynamic program.

GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables).

Vehicle emissions and fuel economy in real-world driving conditions are currently under considerable scrutiny. Key to achieving optimum performance for a given hardware set and control scheme is a calibration that optimizes controller gains such that inputs are scheduled over the operating space to minimize emissions and maximize fuel economy. Generating a suitable calibration requires data that is both precise and accurate, this data is used to generate models that are deployed as part of the calibration optimization process. This paper evaluates the repeatability of typical steady-state measurements used for calibration of engine controllers that will ultimately determine vehicle level emissions for homologation include Real Driving Emissions (RDE). Stabilization requirements as indicated by three different measurements are evaluated and shown to be different within the same experiment, depending on the metric used.

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.

Wet Clutches are used in automotive powertrains to enable compact designs and efficient gear shifting. During the slip phase of engagement, significant flash temperatures arise at the friction disc to separator interface because of dissipative frictional losses. An important aspect of the design process is to ensure the interface temperature does not exceed the material temperature threshold at which accelerated wear behavior and/or thermal degradation occurs. During the early stages of a design process, it is advantageous to evaluate numerous system and component design iterations exposed to plethora of possible drive cycles. A simulation tool is needed which can determine the critical operational conditions the system must survive for performance and durability to be assured. This paper describes a time-efficient multiphysics model developed to predict clutch disc temperatures with a runtime in the order of minutes.

E-mobility is a game changer for the automotive domain. It promises significant reduction in terms of complexity and in terms of local emissions. With falling prices and recent technological advances, the second generation of electric vehicles (EVs) that is now in production makes electromobility an affordable and viable option for more and more transport mission (people, freight). Still, major challenges for large scale deployment remain. They include higher maturity with respect to performance (e.g., range, interaction with the grid), development efficiency (e.g., time-to-market), or production costs. Additionally, an important market transformation currently occurs with the co-development of automated driving functions, connectivity, mobility-as-a-service. New opportunities arise to customize road transportation systems toward application-driven, user-centric smart mobility solutions.

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.