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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.

Encapsulations of E-drive systems are gaining importance in electric mobility, since they are simple measure to improve the noise behavior of the drive. Current experimental evaluation methods however pose substantial challenges for the test personnel and are associated with considerable effort in both time and cost. Evaluating the encapsulation on an e-drive test bed, for example, requires a functional e-drive and test bed resources. Evaluations in the vehicle on the other hand make objective assessments difficult and are subject to increasingly limited availability of prototype vehicles fit for NVH testing. To overcome these challenges, AVL has developed a new experimental evaluation method for the NVH efficiency of e-drive encapsulations. In this method, the e-drive is freely suspended in a semi-anechoic chamber and its structure is excited using shakers while the radiated noise with and without encapsulation is measured.

Vehicle powertrain electrification is considered one of the main measures adopted by vehicle manufacturers to achieve the CO2 emissions targets. Although the development of vehicles with hybrid and plug-in hybrid powertrains is based on existing platforms, the complexity of the system is significantly increased. As a result, the demands of testing during the development and calibration stages is getting significantly higher. To compensate that, high-fidelity simulation models are used as a cost-effective solution. This paper aims to present the methodology followed for the development of a rule-based energy management controller for a plug-in hybrid electric vehicle (PHEV), and to describe the experimental campaign carried out with this passenger car. The controller is implemented in a vehicle simulation model that is parametrized to replicate the operation of the vehicle.

Traditionally, Electric Machine design has primarily focused on factors like efficiency, packaging, and cost, often neglecting the critical aspects of Noise, Vibration, and Harshness (NVH) in the early decision-making stages. This disconnect between E-Machine design teams and NVH teams has consistently posed a challenge. This paper introduces an innovative workflow that unifies these previously separate domains, facilitating comprehensive optimization by seamlessly integrating NVH considerations with other E-Machine objectives, such as electromagnetic compatibility (EMC). This paper highlights AVL's approach in achieving this transformation and demonstrates how this integrated approach sets a new standard for E-Machine design. The presented approach relies on AI-driven algorithms and computational tools.

In 2023, the European Union set more ambitious targets for reducing greenhouse gas emissions from passenger cars: the new fleet-wide average targets became 93.6 g/km for 2025, 49.5 g/km in 2030, going to 0 in 2035. One year away from the 2025 target, this study evaluates what contribution to CO2 reduction was achieved from new conventional vehicles and how to interpret forecasts for future efficiency gains. The European Commission’s vehicle efficiency cost-curves suggest that optimal technology adoption can guarantee up to 50% CO2 reduction by 2025 for conventional vehicles. Official registration data between 2013 and 2022, however, reveal only an average 14% increase in fuel efficiency in standard combustion vehicles, although reaching almost 23% for standard hybrids. The smallest gap between certified emissions and best-case scenarios is of 14 g/km, suggesting that some manufacturers’ declared values are approaching the optimum.

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.

Validation of powertrain systems is nowadays performed with specific durability relevant load cycles, which represent the lifetime requirement of individual powertrain components. The definition of such durability relevant load cycles, which are used for vehicle testing should ideally be based on the actual vehicle's usage. Recording driving cycles within a vehicle is one of the most typical ways of collecting vehicle usage and relevant end customer behavior, but the generation of such measured vehicle data can be time consuming. In addition, this method of capturing on-road measurements has limitations in the variation of vehicle loadings (e.g., number of passengers, luggage, trailer usage etc.). Especially for new applications, entering new target markets, these kinds of in-vehicle measurements are not possible in early development stages, as the required vehicle or powertrain configuration is not available in hardware or incapable of measurements.

This paper presents calculations of external car aerodynamics by using the Partial-Averaged Navier-Stokes (PANS) variable resolution model in conjunction with the Finite Volume (FV) immersed-boundary method. The work presented here is the continuation of the study reported in Basara et al. [1, 2]. In that work, it was shown that the same accuracy of predicted aerodynamic forces could be achieved for both types of computational meshes, the standard body-fitted mesh and the immersed boundary (IB) Cartesian mesh, by using the Reynolds-Averaged Navier-Stokes (RANS) k-ζ-f model as well as by using the Partially-Averaged Navier-Stokes (PANS) method. Based on the accuracy achieved, Basara et al. [2] concluded that further work could focus on evaluating the turbulence modelling on the immersed boundary meshes only.

Stoichiometric natural gas (CNG) engines are an attractive solution for heavy-duty vehicles considering their inherent advantage in emitting lower CO2 emissions compared to their Diesel counterparts. Additionally, their aftertreatment system can be simpler and less costly as NOx reduction is handled simultaneously with CO/HC oxidation by a Three-Way Catalyst (TWC). The conversion of methane over a TWC shows a complex behavior, significantly different than non-methane hydrocarbons in stoichiometric gasoline engines. Its performance is maximized in a narrow A/F window and is strongly affected by the lean/rich cycling frequency. Experimental and simulation results indicate that lean-mode efficiency is governed by the palladium’s oxidation state while rich conversion is governed by the gradual formation of carbonaceous compounds which temporarily deactivate the active materials.

The degradation rate of a Li-ion battery is a complex function of temperature and charge/discharge rates over its lifetime. There is obviously a keen interest in predictive electrochemical ageing models that account for known degradation mechanisms, primarily linked with the Solid Electrolyte Interface (SEI) formation and Li-plating, which are highly dependent on the cell temperature. Typically, such ageing models are formulated and employed at pack or cell level, neglecting intra-cell and cell-to-cell thermal and electrical non-uniformities. On the other hand, thermal management techniques can mitigate ageing by maintaining the battery pack within the desired temperature window either by cooling or heating. Inevitably, the cooling of the battery pack by conventional heat exchangers will introduce temperature non-uniformities that may in turn result in undesired intra-cell and/or cell-to-cell health non-uniformities.

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.

The degradation rate of Li-ion batteries and therefore their useful life depends on many parameters, including temperature, charge/discharge rates, the chemistry and microstructure of electrodes. The importance of understanding these mechanisms explains the large interest in developing predictive electrochemical ageing models accounting for the known deterioration mechanisms, mainly related to SEI layer formation and Li-plating. Usually, these ageing models are developed and applied at cell level assuming perfect uniformity in all dimensions apart from the through-plane direction. In this work, we extend the model to all dimensions within the cell to account for intra-cell non-uniformities in terms of local temperature and current. However, the temperature distribution of a cell within a battery pack depends on the interaction with its environment, which typically involves active cooling via an external fluid circulation within a channel network.

This paper investigates how different on-board energy management system (EMS) algorithms can affect the total energy consumption considering propulsion, heating, ventilation, and air conditioning (HVAC) operation and thermal comfort requirements. Firstly, an integrated plug-in hybrid electric vehicle (PHEV) powertrain and HVAC model including vehicle cabin has been developed as a demonstrator. Two different EMS algorithms - namely a rule-based and an equivalent consumption minimization strategy (ECMS) one - are applied to the integrated PHEV model and evaluated under different environmental conditions. The results showed that the HVAC system operation affects the total energy consumption benefits when ECMS algorithm is used over the rule-based. ECMS reduces the total energy consumption by 2.5% compared to rule-based without HVAC operation, while the total energy consumption reduction changes to 5.3% and 6.3% when HVAC provides heating and cooling power respectively.

In recent years, the use of high-power inverters has become increasingly prevalent in vehicles applications. With the increasing number of electric vehicle models comes the need for efficient and reliable testing methods to ensure the proper functioning of these inverters. One such method is the use of Hardware-in-the-Loop (HiL) environments, where the inverter is connected to a simulated environment to test its performance under various operating conditions. HiL testing allows for faster and more cost-effective testing than traditional methods and provides a safe environment to evaluate the inverter's response to different scenarios. Further, in such an environment, it is possible to specifically stimulate those system states in which conflicts between the lines arise regarding the ideal system parametrization. By combining HiL testing with design-of-experiments and modelling methods, the propulsion system can hence be optimized in a holistic manner.

Cellular exposure to diluted exhaust gas is a promising method to assess the adverse effects of road traffic on human health. To fully understand the potential correlation between emission patterns, vehicle technologies and cellular toxicity in real-world scenarios, further research is needed. This study evaluates the toxicity of exhaust emissions from two advanced technology vehicles in real-world driving conditions. One vehicle is a gasoline direct injection (GDI) with a particle filter (GPF), while the other is a gasoline port fuel injection (PFI) hybrid without a GPF. The vehicles were tested on a chassis dyno using a Real Driving Emissions (RDE) test cycle that replicates on-road conditions. The test cycle included both cold and hot starting engine conditions. Human epithelial A549 cells were exposed to diluted exhaust using an Air Liquid Interface (ALI) system to assess toxicity. Τhe particle dose during cell exposure simulated human inhalation in an urban environment.