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The automotive industry faces development challenges due to emerging technologies, regulatory demands, societal trends, and evolving customer mobility needs. These factors contribute to a wide range of vehicle variants and increasingly complex powertrains. The layout of a vehicle is usually based on standardized driving cycles such as WLTC, gradeability, acceleration test cases, and many more. In real-world driving cycles, however, this can lead to limitations under certain boundary conditions. To ensure that all customer requirements are met, vehicle testing is conducted under extreme environmental conditions, e.g., in Sweden or Spain. One way to reduce the development time while ensuring high product quality and cost-effectiveness is to use model-based methods for the comprehensive design of powertrains. This study presents a layout methodology using a top-down approach. Initially, powertrain-relevant requirements for an exemplary target customer are translated into a specification sheet with specific test cases. An overall vehicle model with detailed thermal sub-models is developed to evaluate the different requirements. A baseline design for a C-segment plug-in hybrid vehicle was developed as part of the FVV research project HyFlex-ICE using standardized test cases, highlighting the influence of customer profiles on the design outcome through varying weighting factors. The target customer’s design is analyzed in four real driving scenarios, considering variations in parameters such as the ambient temperature, traffic, driver type, trailer pulling, and battery state-of-charge, to assess their influence on the target variables. In the next step, the potential of hardware technologies and predictive driving functions is examined in selected driving scenarios based on the identified constraints of the baseline design. As a result, four application-specific technology packages (Cost neutral, Cold country, Hot country, and Premium) for different customer requirements and sales markets are defined, which, finally, demonstrates the applicability of the holistic methodology.

The systematic analysis of measurement data allows a large amount of information to be obtained from existing measurements in a short period of time. Especially in vehicle development, many measurements are performed, and large amounts of data are collected in the process of emission calibration. With the introduction of Real Driving Emissions Tests, the need for targeted analysis for efficient and robust calibration of a vehicle has further increased. With countless possible test scenarios, test-by-test analysis is no longer possible with the current state-of-the-art in calibration, as it takes too much time and can disregard relevant data when analyzed manually. In this article, therefore, a methodology is presented that automatically analyzes exhaust measurement data in the context of emission calibration and identifies emission-related critical sequences. For this purpose, moving analyzing windows are used, which evaluate the exhaust emissions in each sample of the measurement. The detected events are stored in tabular form and are particularly suitable for condensing the collected measurement data to a required amount for optimization purposes. It is shown how different window settings influence the amount and duration of detected events. With the example used, a total amount of 454 events can be identified from 60 measurements, reducing 184,623 s of measurements to a relevant amount of 12,823 s.

Abstract The energy demand of the auxiliaries of battery electric vehicles can account for a significant share of the total energy demand of a trip and must be taken into account for the prediction of the vehicle's remaining driving range or the implementation of predictive driving functions. This paper investigates a method that uses system identification and neural networks with bidirectional long short‐term memory layers to predict the power requirements of the auxiliaries depending on information that is known prior to the trip. By using a self‐learning, data‐driven approach as well as data that can be measured without additional instrumentation, a prediction is made possible without the need to design detailed physical models in advance. Additionally, a rule‐based allocation of the training data based on environmental conditions is implemented, which serves to adapt individual models to different climatic modes of the thermal system. The potential of the method is demonstrated for three different systems showing a prediction accuracy of on average 3% to 8% in terms of energy, while the deviation of the predicted power consumption is on average about 500 watts. Due to the complete automation of the process, a further increase in prediction accuracy can be expected.

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated.

The transport sector faces two critical issues: a limited supply of fossil fuels and high greenhouse gas (GHG) emissions. Additionally, the demand for freight transport is steadily increasing, ultimately leading to higher GHG emissions. Since these emissions promote climate change, reducing the GHG emissions from the transport sector is necessary. Renewable drop-in fuels can play an essential role in this regard as those are CO2 neutral. Since these fuels come from so-called renewable sources, this represents a way to reduce carbon dioxide emissions from the current vehicle fleet to meet the EU’s Green Deal goals to become climate neutral by 2050. The drop-in fuels from renewable sources and later the purely renewable fuels serve as a bridging technology in this context.With this in mind, experiments were conducted with a Heavy-Duty Single Cylinder Engine (HD-SCE). The effects of four different renewable fuels or fuel blends – 93% RF/7% UCOME, 60% B0-Diesel/40% RF blend, 70% Diesel/30% Octanol blend and 100% Octanol – on engine performance and raw emissions were studied in comparison to fossil Diesel fuel. The investigations were conducted at three different load points — Rated Power (RP), best Brake Thermal Efficiency (BTE) and Cruise Point, covering all the relevant load points for HD engines. For all load points, the use of renewable fuels resulted in lower carbon dioxide (CO2), hydrocarbon (HC), carbon monoxide (CO) and FSN compared to fossil Diesel due to the fuel-borne oxygen and the lower C/H ratio of these alternative fuels. The blend of 60% B0-Diesel-40% RF shows the highest efficiency due to the paraffinic fuel structure, the fuel-borne oxygen, the higher calorific value, and the high cetane number. 100% Octanol resulted in a reduction in FSN by a factor of 3. All renewable fuels show a GHG emission reduction potential of around 2.5% to 5.5% in the Tank-to-Wheel (TtW) analysis.

In hybrid vehicle development, the operating strategy has a decisive role in meeting the development goals, such as compliance with emission standards and high energy efficiency. A considerable number of interactions and cross-influences on other topics, such as emissions, on-board diagnostics, or drivability, must be considered during the calibration process. In this context, the given time constraints pose further challenges. To overcome these, approaches for virtualization of the calibration process are an effective measure. For this purpose, in the current study, a real engine control unit is embedded into a virtual simulation environment on so-called hardware-in-the-loop (HiL) testbenches, which allow virtual calibration and validation of the complete target vehicle. In this context, the paper presents a novel method for virtual calibration of operating strategies for hybrid-electric propulsion systems. This includes an innovative multi-criterial approach that considers the requirements of several development tasks, such as emission and OBD calibration. Measurement data for this optimization is generated on a HiL testbench setup tailored for the described methodology, including both the electrical setup and the simulation environment. To validate the selection of modeling approaches and the parametrization, the simulation environment is operated in open loop. The results of the open loop validation show promising behavior regarding the proposed use case. Finally, the presented methodology is evaluated regarding time and cost savings compared to a conventional approach.

Abstract Compared with silicon‐based Insulated Gate Bipolar Transistors (IGBTs), silicon carbide (SiC) Metal‐Oxide‐Semiconductor Field‐Effect Transistors (MOSFETs) are characterized by higher operating temperatures, switching speeds and switching frequencies, and are considered the next evolutionary step for future electric drives. The application of SiC MOSFETs in the field of electrified vehicles has brought many benefits, such as higher efficiency, higher power density, and simplified cooling system, and can be seen as an enabler for high‐power fast battery charging. This article reviews the benefits of SiC MOSFETs in different electrified vehicle (EV) application scenarios, including traction inverters, on‐board converters, and off‐board charging applications. However, replacing Si‐IGBTs with SiC MOSFETs introduces several new technical challenges, such as stronger electromagnetic interference (EMI), reliability issues, potential electric machine insulation failure due to high transient voltages, and cooling difficulties. Compared to mature silicon‐based semiconductor technologies, these challenges have so far hindered the widespread adoption of SiC MOSFETs in automotive applications. To fully exploit the advantages of SiC MOSFETs in automotive applications and enhance their reliability, this paper explores future technology developments in SiC MOSFET module packaging and driver design, as well as novel electric machine drive strategies with higher switching frequencies, and optimized high‐frequency machine design.

To increase system efficiency and power density, the cathode air path of Polymer Electrolyte Membrane Fuel Cells (PEM FCs) is supercharged using electrically driven air bearing centrifugal compressors. To maximize system efficiency, the cathode air supply system must be designed to optimally fulfill the requirements of the PEM FC system while obeying the design constraints imposed by the electric compressor drive and the air bearing system. This article proposes a dedicated design process for PEM FC cathode air compressors. Using physically based component models, the impact of varying cathode stoichiometry and operating pressure on PEM FC system performance is assessed to derive the system efficiency optimal compressor operating strategy. The centrifugal compressor stage is subsequently designed to achieve optimum efficiency on this operating line using meanline performance models and three-dimensional computational fluid dynamics simulations. Novel test procedures and measurement equipment are employed to validate the compressor design. The design process is demonstrated using a PEM FC passenger car application as an example. It is shown that significant performance and efficiency gains are achievable when tailoring the cathode air supply system to the application at hand. In the given example, effective compressor efficiency is increased by Δηeff = 12%. Along with an optimized compressor operating strategy, an overall PEM FC system efficiency gain of Δηsys = 2.7% is achieved.

Gaseous and semi-volatile organic compounds emitted by the transport sector contribute to air pollution and have adverse effects on human health. To reduce harmful effects to the environment as well as to humans, renewable and sustainable bio-hybrid fuels are explored and investigated in the cluster of excellence "The Fuel Science Center" at RWTH Aachen University. However, data on the effects of bio-hybrid fuels on human health is scarce, leaving a data gap regarding their hazard potential. To help close this data gap, this study investigates potential toxic effects of a Ketone-Ester-Alcohol-Alkane (KEAA) fuel blend on A549 human lung cells. Experiments were performed using a commercially available air-liquid interface exposure system which was optimized beforehand. Then, cells were exposed at the air-liquid interface to 50-2000 ppm C3.7 of gaseous KEAA for 1 h. After a 24 h recovery period in the incubator, cells treated with 500 ppm C3.7 KEAA showed significant lower metabolic activity and cells treated with 50, 250, 500 and 1000 ppm C3.7 KEAA showed significant higher cytotoxicity compared to controls. Our data support the international occupational exposure limits of the single KEAA constituents. This finding applies only to the exposure scenario tested in this study and is difficult to extrapolate to the complex in vivo situation.

The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers.

This paper is based on the work presented at EVS36 in Sacramento. The core of the work deals with the cabin climate control of battery electric vehicles (BEV) using model predictive control (MPC) approaches. These aim to reduce the energy demand for cabin air conditioning while maintaining comfort and air quality. The first step briefly overviews model predictive control approaches and the respective fundamentals. Afterward, the modeling for the system dynamics is explained. The challenge for the system model considering humid air is discussed, and the first implementation method is presented. With the added equations for the air quality and humidity, a logic to prevent window fogging was developed to improve safety. Ultimately, model-in-the-loop (MiL) investigations identified an energy-saving potential of up to 15.4% for cold and 39.7% for hot conditions compared to a rule-based strategy. In addition, the investigations carried out showed that it was also possible to improve indoor comfort by specifically influencing the air quality and humidity. Together with the safety criteria introduced to prevent window fogging, it was possible to present a strategy that can significantly improve thermal management for the cabin in modern BEVs.

Proton exchange membrane fuel cells are a promising technology for future transportation applications. However, start-up procedures that are not optimized for low temperatures can lead to the early failure of the cells. Detailed CFD models can support the optimization of cold start procedures, but they often cannot be solved in a stable way due to their complexity. One-dimensional (1D) models can be calculated quickly but are simplified so that the behavior of the cells can no longer be determined accurately. In this contribution, a coupling between a 2D CFD model of the gas channels and a 1D model of the Membrane Electrode Assembly (MEA) is realized. This method allows not only to determine the location and amount of the condensed water but also to calculate the exact concentration of the reactant gases along the channels. The investigations show that the concentrations of the gases and the relative humidities in the gas channels are strongly influenced by the current density. It has been found that it is not possible to avoid the formation of liquid water at low operating temperatures by controlling the current density.

With the Euro 7 pollutant emission legislation currently under discussion, advanced and more efficient exhaust aftertreatment systems are being developed. The technologies required for these are leading to an increase in the number of components and control systems requiring diagnoses strategies under the on-board diagnostics (OBD) legislation. With concurrent shorter development times and significant reductions in budgets allocated to conventional powertrain development, challenges in the field of OBD calibration and verification are already rising sharply. In response to these challenges, hardware-in-the-loop (HiL) approaches have been successfully introduced to support and replace conventional development methods. The use of complex simulation models significantly improves the quality of calibrations while minimizing the number of required prototype vehicles and test resources, thus reducing development costs. This paper presents a feasibility study for moving OBD-related calibration and validation tasks from the vehicle to a HiL platform. In this context, the calibration and verification process of an active diagnostic for monitoring the condition of the three-way catalyst (TWC) and the oxygen sensors in the exhaust aftertreatment system is presented. It is shown that all relevant signals are simulated with sufficient accuracy to ensure a robust transfer from the vehicle to a HiL test bench. Special attention is given to the simulation of aged components and their influence on the emission behavior of the system. Furthermore, it is discussed that transferring OBD tasks from the vehicle to the HiL test bench could result in significant savings in development time and a reduction in the number of physical prototype vehicles and test resources required.

Mechanics plays a crucial role in the performance and lifespan of lithium-ion battery (LIB) cells. Thus, it is important to address the interplay between electrochemistry and mechanics in LIBs, especially when aiming to enhance the energy density of electrodes. Accordingly, this work introduces a framework for a fully coupled electro-chemo-mechanical heterogeneous 3D model that allows resolving the inhomogeneities accompanied by electrochemical and mechanical responses of LIB electrodes during operation. The model is employed to numerically study the mechanical degradation of a nickel manganese cobalt (NMC) cathode electrode, assembled in a half-cell, upon cycling. As opposed to previous works, a virtual morphology for a high-energy electrode with low porosity is developed in this study, which comprises distinct domains of active material (AM) particles, the carbon-binder domain (CBD), and the pore domain to resemble real commercial electrodes. It is observed that the mechanical strain mismatch between irregularly and randomly positioned AM particles and the CBD might lead to local contact detachment. This interfacial gap, in combination with the diminishing contact strength over cell cycling, continuously deteriorates the electrode performance upon cycling by impedance rise and capacity drop. In agreement with previous experimental reports, the presented simulation results exhibit that the contact loss mostly takes place in the regions closer to the separator. Eventually, the resulting gradual capacity drop and change in impedance spectrum over cycling, as the consequence of interfacial gap formation, are discussed and indicated.

Understanding cycle-to-cycle variations (CCV) is of practical importance for the combustion of fossil and renewable fuels, as increasingly stringent emission regulations require reductions in the negative effects of such variations. The subject of this study is the flow around inlet valves, since oscillations of such inlet flows affect the flow structure in the cylinder and are thus one of the causes of CCV. To this end, a parametric analysis of the influences of the mass flow rate and valve lift of two parallel engine intake valves on the flow structures is performed. This follows on from an earlier similar study where the flow around a single intake valve was investigated. To analyse the flow behaviour and, in particular, the interactions of the flow leaving these two valves, an optical test rig for 2D particle image velocimetry (PIV) and a large eddy simulation (LES) are used. Proper orthogonal decomposition (POD), together with a quadruple decomposition and the Reynolds stress transport equations, are used to study the turbulence phenomena. The PIV and LES results are in good agreement with each other. The detailed LES analysis of the flow structures shows that, for small valve lifts, the flow separates along the whole perimeter of the intake valve, and for larger valve lifts, the flow escapes only to one side. This is, for combustion engines with the tumble concept, the stage at which the tumble movement develops. Moreover, the flow structures are strongly influenced by the valve lift, while they are unaffected by the variation in the mass flow. The turbulent kinetic energy in the flow field increases quadratically with a decreasing valve lift and increasing mass flow. The large, high-energetic flow structures are particularly dominant near the jet, and the small, low-energetic structures are homogeneously distributed within the flow field. The specific Reynolds stress transport equation shows the limitations of two-dimensionality and large timesteps in the PIV results and the limitations of the LES model.

Freeze start is a challenge in the commercialization of PEM fuel cells. In this study, ice formation in cell layers is investigated through experiments and simulations. Segmentation of the fuel cell on the test bench allows to determine the local distributions of current density and high frequency resistance over the active cell area. The location and timing of ice formation are analyzed in the experiments. It is shown that the formation of ice lenses can be detected by local measurements of the high frequency resistances. Then, a multiphysical CFD model is built and validated with the measurements and the commonalities and differences between the model results and the experiments are studied. It is shown that the model determines the freeze start behavior very well in wide operating ranges. Together with the findings from the experimental investigations, the model will finally be used to investigate local ice formation in detail.

To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.

The microstructures of porous electrodes in lithium-ion cells strongly affect their electrochemical performance. Experimental tomography techniques to investigate the microstructure during electrode development is costly and time consuming. To address this issue, a numerical method is presented to create a digital morphology to realize a realistic microstructure. In this study, the spherical harmonics in a straightforward mathematical approach are proposed to develop the virtual 3D morphology of the electrode’s heterogeneous structure. The introduced method offers a numerically light procedure which enables effective iterative virtual testing and optimization. The generated morphology model is parameterized to reproduce a NMC cathode microstructure observed in the literature. The electrode model allows evaluation of the spatially resolved geometric, transport and electric potential characteristics of the microstructure. The computed characteristics are employed to improve the parametrization of the continuum model as the most widely used physics-based model. For this purpose, the electrochemical impedance spectra of a lithium foil/separator/NMC half-cell is virtually modeled by heterogeneous and continuum approaches. Then, the modified continuum model is compared to the heterogeneous model as a benchmark, in terms of the kinetics and transport characteristics underlying the electrochemical impedance spectra. The modified continuum model shows an improved response in both frequency and time domains.

Development challenges in the automotive industry are constantly increasing due to the high number of vehicle variants, the growing complexity of powertrains, and future legal requirements. In order to reduce development times while maintaining a high level of product quality and financial feasibility, the application of new model-based methods for virtual powertrain calibration is a particularly suitable approach. In this context, TME and FEV combine advanced thermal management models with electronic control unit (ECU) models for model-in-the-loop applications. This paper presents a development process for ECU and on-board diagnostics (OBD) functions of thermal management systems in hybrid electric vehicles. Thanks to the highly accurate 1D/3D-models, optimal control strategies for electrically actuated components can be developed in early development phases. Virtual sensors for local temperatures are developed for the ECU software to enable a cost-effective use of dedicated control functions. Furthermore, an application for OBD cooling system leakage detection is shown. Finally, the transferability of the methodology to a battery cooling system is demonstrated.

Due to stringent emission regulations, it is of practical significance to understand cycle-to-cycle variations in the combustion of fossil or renewable fuels to reach future emission regulations. The present study aims to conduct a parametric investigation to analyse the influence of the valve lift and different mass flows of an inlet valve of the test engine “Flex-OeCoS” on the flow structures. To gain a deeper understanding of the flow behaviour, an optical test bench for 2D Particle Image Velocimetry (PIV) and a Large Eddy Simulation (LES) are used. Turbulence phenomena are investigated using Proper Orthogonal Decomposition (POD) with a quadruple decomposition and the Reynolds stress transport equation. The results show good agreement between the PIV and LES. Moreover, the main flow structures are primarily affected by valve lift while being unaffected by mass flow variation. The turbulent kinetic energy within the flow field increases quadratically to the mass flow and to the decreasing valve lift, where large high-energetic flow structures are observed in the vicinity of the jet and small low-energetic structures are homogeneously distributed within the flow field. Furthermore, the convective flux, the turbulent diffusive flux, the rate of change, and the production of specific Reynolds stress are the dominant terms within the specific Reynolds stress transport equation.

Future vehicle powertrain systems with internal combustion engines must achieve higher efficiencies and further reduced pollutant emissions. This will require the application of new advanced technologies. Against this background, this paper presents a holistic approach to reduce temporally the wall heat losses, and hydrocarbon and carbon monoxide emissions with thermal coatings on the combustion chamber walls. For this purpose, an yttria-stabilized zirconia (YSZ) coating is applied and evaluated by different methods. The thin layer in combination with the low thermal effusivity of the material allows the wall temperature to follow the gas temperature and perform a so-called thermal swing. The interaction between an uncoated and a YSZ-coated wall with the flame front as well as partially burned gas was investigated. First, in terms of the coating’s potential to reduce the flame quenching distance using an optical method in a constant volume combustion chamber. Second, regarding its influence on the near-wall gas composition, which was analyzed with in-situ diffuse reflectance infrared Fourier transform spectroscopy measurements and a fast gas sampling technique on a single-cylinder engine. From this, it could be derived that the quenching distance can be reduced by 10% at ambient conditions and by 5% at an elevated temperature of 200 °C by using the coating. These findings also support the results that have been obtained by the near-wall gas composition measurements, where a reduced total hydrocarbon emission was found with the applied coating.

This microarticle shows the potential of battery cell-to-pack design approaches by means of a systematic investigation at different depths of implementation. For this purpose, battery concepts are created under cell-to-pack aspects based on a conventional concept and investigated with regard to the geometric layout and the packaging density at pack level. Implementation options range from simply omitting the module housing while keeping the subdivision of the original modules up to a pure block design. For the studies, an object-oriented tool was developed that models the components of battery systems as class objects and provides various functions for calculating parameters and generating graphics for layout visualization.

The Euro 7 legislation and the Zero-Impact Emissions concept aim at significantly improving air quality. Technologies that reduce pollutant emissions beyond current gasoline passenger cars have already been intensively investigated, but a holistic system layout considering extended boundary conditions is missing so far. This paper therefore develops technical solutions to achieve a Euro 7 scenario and Zero-Impact Emissions for a 2030+ vehicle. First, challenging test scenarios are identified to develop compliant vehicles. The scenarios cover extreme conditions in real-world driving, such as hot and cold ambient conditions, stop-and-go in rural areas or high speed and steep gradients on highways. Different technology options are discussed and selected for the investigations. An empirical–physical simulation model for the exhaust gas aftertreatment system is extended with new technologies, such as an electrical heater disc in front of the catalyst or a burner in the exhaust system. In addition to stoichiometric engine operation and increased catalyst volume, the results show that the expected Euro 7 regulations can be achieved in all extreme scenarios by combining additional exhaust gas heating with engine power limitation or pre-heating. Moreover, even Zero-Impact Emissions are achieved in most cases with the same technology options.

Injection of fuel into the exhaust system is used to support the operation of exhaust gas aftertreatment systems. When injecting a fluid into the exhaust system, one major challenge is to design the exhaust system for optimal fuel distribution and to define an appropriate injection pressure. Simulation techniques are a suitable measure to support the corresponding development process. Hence, the project “Exhaust Fuel Injection” was initiated. Project target is the alignment of simulation methods and measurement techniques to predict the HC distribution in radial and axial direction at the inlet of a catalyst. This includes the definition of suitable measurement techniques to determine the hydrocarbon concentrations upstream of a catalyst. Transient 3D Computational Fluid Dynamics (CFD) simulation was used to model the fuel injection into a purpose-built exhaust system. The simulation results of the distribution of hydrocarbons at catalyst inlet in radial and axial direction are compared with measurements performed at an engine test bench. In total 18 variations were simulated and measured and twelve of them showed deviations in the uniformity index (UI) below 2 %. Only two variations resulted in deviations in UI above 3%. The trends in axial distribution showed good correlation between test bench and simulation. Especially the influence of injection pressure and operating point was predicted well by the simulation.

Discharge of electric field generated by a spark plug is one of the most important and influential factors to determine combustion behavior of SI engines, as it initiates flame kernels by supplying energy to the air-fuel mixture. A discharge model is required to analyze this process with the help of numerical simulation. A new probabilistic discharge model has been developed, in which the probability of the breakdown is proportional to the intensity of the local electric field. A wide variety of discharges, such as air gap discharge, surface discharge and branching discharge like corona, can be calculated with the developed model. Consequently a detailed analysis of the process from discharge to combustion can be achieved.

In addition to SCR systems, lean NOX traps (LNTs) are also used for exhaust aftertreatment of lean burn internal combustion engines to sustainably reduce NOX emissions. Modern LNTs consist of different functional compounds to maximize the performance during NOX storage and regeneration. Based on the material analysis of a serial production LNT, PGM loaded BaO, Al2O3, MgAl2O4, and CeO2 were identified as the main base materials. In this paper, the NOX storage capacity (NSC) of these compounds is investigated both as single catalysts and as physical mixtures to identify possible synergistic effects. Therefore, commercially available support materials were loaded with Platinum and tested in granular form under realistic conditions. To optimize the performance by reducing the diffusion pathways for NOX molecules during storage, PGM, BaO, and Ceria were combined in a composite by the incipient wetness impregnation of alumina. As a result, the temperature dependent NSC of the commercial LNT could be reached with the Pt/Rh/Ba10Ce25/Al2O3 infiltration composite, while reducing the oxygen storage capacity by about 45%. Without the additional Rhodium coating, the low-temperature NSC was insufficient, highlighting the important contribution of this precious metal to the overall performance of LNTs.

The efficiency and robustness of selective catalytic reduction (SCR) by NH3 catalysts for exhaust gas purification, especially of heavy-duty diesel engines, will continue to play a major role, despite the increasing electrification of powertrains. With that in mind, the effect of the synthesis scale on commercially available Cu-exchanged chabazite catalysts for SCR was investigated through physicochemical characterizations and catalytic tests. During hydrothermal aging, both industrial and lab-scale prepared catalysts underwent structural dealumination of the zeolite framework and redistribution of the Al sites. Although both catalysts demonstrated similar NO conversion activity under SCR conditions, the lab-scale catalyst showed higher selectivity and lower activity in NH3 oxidation. Variations in N2O formation and NH3 oxidation rate were found to correlate with the formation of different copper species, and the compositions become less controllable in industrial-scale process. This case study focused on routes of ion exchange, and the results provide new insights into catalytic performance of the industrially-produced zeolites.

Hydrogen as carbon-free fuel is a very promising candidate for climate-neutral internal combustion engine operation. In comparison to other renewable fuels, hydrogen does obviously not produce CO2 emissions. In this work, two concepts of hydrogen internal combustion engines (H2-ICEs) are investigated experimentally. One approach is the modification of a state-of-the-art gasoline passenger car engine using hydrogen direct injection. It targets gasoline-like specific power output by mixture enrichment down to stoichiometric operation. Another approach is to use a heavy-duty diesel engine equipped with spark ignition and hydrogen port fuel injection. Here, a diesel-like indicated efficiency is targeted through constant lean-burn operation. The measurement results show that both approaches are applicable. For the gasoline engine-based concept, stoichiometric operation requires a three-way catalyst or a three-way NOX storage catalyst as the primary exhaust gas aftertreatment system. For the diesel engine-based concept, state-of-the-art selective catalytic reduction (SCR) catalysts can be used to reduce the NOx emissions, provided the engine calibration ensures sufficient exhaust gas temperature levels. In conclusion, while H2-ICEs present new challenges for the development of the exhaust gas aftertreatment systems, they are capable to realize zero-impact tailpipe emission operation.

Filtration of cathode air is one of the challenges in operating proton-exchange membrane (PEM) fuel cells. The poisoning with air contaminants can lead to rapid performance degradation and initiate an aging process of the fuel cell. Various commercially available cathode filters are being tested in a laboratory gas test bench within the research project X-EMU (03B10502B and 03B10502B2). A literature review of harmful gas contaminants in the air used for the oxygen reduction reaction (ORR) on the cathode side was conducted. Experimental investigations took place at 40 °C with synthetic humid air containing low concentration contaminants such as ammonia, nitrogen dioxide, carbon monoxide, sulfur dioxide, hydrogen sulfide, and toluene. Test durations varied from 3 to 24 h depending on the filtration efficiency. Each gas contaminant showed different reactions with the investigated filters. The filters did not let sulfur-containing components pass. However, carbon monoxide could not be filtrated by any of the tested filters. The filtration of nitrogen oxides was not efficient for all tested filters, while additional filter materials were essential for a successful filtration of ammonia. Comparative results lead to a discussion of possible effects on a fuel cell with an outlook on optimization of the filtration behavior.

The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential. An approach for such a combination is presented in this paper. Together with a Hardware-in-the-Loop (HiL) test bench, the novel methodology enables a targeted calibration process, specifically designed to address calibration challenges of hybridized powertrains. Virtual tests executed on a HiL test bench are used to efficiently generate data characterizing the behavior of the system under various conditions with a statistically based evaluation identifying white spots in measurement data, used for calibration and emission validation. In addition, critical sequences are identified in terms of emission intensity, fuel consumption or component conditions. Dedicated test scenarios are generated and applied on the HiL test bench, which take into account the state of the system and are adjusted depending on it. The example of one emission calibration use case is used to illustrate the benefits of using a HiL platform, which achieves approximately 20% reduction in calibration time by only showing differences of less than 2% for fuel consumption and emission levels compared to real vehicle tests.

In view of the deliberations on new Euro 7 emission standards to be introduced by 2025, original equipment manufacturers (OEMs) are already hard at work to further minimise the pollutant emissions of their vehicles. A particular challenge in this context will be compliance with new particulate number (PN) limits. It is expected that these will be tightened significantly, especially by including particulates down to 10 nm. This will lead to a substantially increased effort in the calibration of gasoline particulate filter (GPF) control systems. Therefore, it is of great interest to implement advanced methods that enable shortened and at the same time more accurate GPF calibration techniques. In this context, this study presents an innovative GPF calibration procedure that can enable a uniquely efficient development process. In doing so, some calibration work packages involving GPF soot loading and regeneration are transferred to a modern burner test bench. This approach can minimise the costly and time-consuming use of engine test benches for GPF calibration tasks. Accurate characterisation of the particulate emissions produced after a cold start by the target engine in terms of size distribution, morphology, and the following exhaust gas backpressure and burn-off rates of the soot inside the GPF provides the basis for a precise reproduction and validation process on the burner test bench. The burner test bench presented enables the generation of particulates with a geometric mean diameter (GMD) of 35 nm, exactly as they were measured in the exhaust gas of the engine. The elemental composition of the burner particulates also shows strong similarities to the particulates produced by the gasoline engine, which is further confirmed by matching burn-off rates. Furthermore, the exhaust backpressure behaviour can accurately be reproduced over the entire loading range of the GPF. By shifting GPF-related calibration tasks to the burner test bench, total filter loading times can be reduced by up to 93%.

Reducing air pollution caused by emissions from road traffic, especially in urban areas, is an important goal of legislators and the automotive industry. The introduction of so-called “Real Driving Emission” (RDE) tests for the homologation of vehicles with internal combustion engines according to the EU6d legislation was a fundamental milestone for vehicle and powertrain development. Due to the introduction of non-reproducible on-road emission tests with “Portable Emission Measurement Systems” (PEMS) in addition to the standardized emission tests on chassis dynamometers, emission aftertreatment development and validation has become significantly more complex. For explicit proof of compliance with the emission and fuel consumption regulations, the legislators continue to require the “Worldwide Harmonized Light Duty Vehicle Test Cycle” (WLTC) on a chassis dynamometer. For calibration purposes, also various RDE profiles are conducted on the chassis dynamometer. However, the combination of precisely defined driving profiles on the chassis dynamometer and the dynamics-limiting boundary conditions in PEMS tests on the road still lead to discrepancies between the certified test results and the real vehicle behavior. The expected future emissions standards to replace EU6d will therefore force even more realistic RDE tests. This is to be achieved by significantly extending the permissible RDE test boundary conditions, such as giving more weight to the urban section of an RDE test. In addition, the introduction of limit values for previously unregulated pollutants such as nitrogen dioxide (NO2), nitrous oxide (N2O), ammonia (NH3) and formaldehyde (CH2O) is being considered. Furthermore, the particle number (for diameters of solid particles > 10 nm: PN10), the methane (CH4) emissions and emissions of non-methane organic gases (NMOG) shall be limited and must be tested. To simplify the test procedure in the long term, the abandonment of predefined chassis dyno emission tests to determine the pollutant emission behavior is under discussion. Against this background, current testing, validation, and development methods are reviewed in this paper. New challenges and necessary adaptations of current approaches are discussed and presented to illustrate the need to consider future regulatory requirements in today’s approaches. Conclusions are drawn and suggestions for a robust RDE validation procedure are formulated.

Reynolds-averaged Navier–Stokes (RANS) three-dimensional (3D) computational fluid dynamics (CFD) simulations of gaseous emissions from combustion engines are very demanding due to the complex geometry, the emissions formation mechanisms, and the transient processes inside the cylinders. The validation of emission simulation is challenging because of modeling simplifications, fundamental differences from reality (e.g., fuel surrogates), and difficulty in the comparison with measured emission values, which depend on the measuring position. In this study, detailed gaseous emission data were acquired for a spark ignition (SI) direct-injection (DI) single-cylinder engine (SCE) fueled with a toluene reference fuel (TRF) surrogate to allow precise comparison with simulations. Multiple devices in different sampling locations were used for the measurement of average emission concentration, as well as hydrocarbon (HC) cycle- and species-resolved values. A RANS 3D-CFD methodology to predict gaseous pollutants was developed and validated with this experimental database. For precise validation, the emission comparison was performed in the exact same locations as the pollutants were measured. Additionally, the same surrogate fuel used in the measurements was defined in the simulation. To focus on the emission prediction, the pressure and heat release traces were reproduced by calibrating a G-equation flame propagation model. The differences of simulation results with measurements were within 4% for CO2, while for O2 and NO, the deviations were within 26%. CO emissions were generally overestimated probably because of inaccuracies in mixture formation. For HC emissions, deviations up to 50% were observed possibly due to inexact estimation of the influence of the piston-ring crevice geometry. The reasonable prediction accuracy in the RANS context makes the method a useful framework for the analysis of emissions from SI engines, as well as for mechanism validation under engine relevant conditions.

One of the possibilities to increase the efficiency of an internal combustion engine is to enhance its interaction with the charging system. With the help of new advanced simulation tools, the rotor dynamic behavior and bearing friction losses of turbochargers (TC) can be quantified in the early stage of the development process. This procedure enables virtual bearing development, leading to shortened development times and reduced testing costs. This paper presents a detailed view of the findings in current research; focusing on rotor dynamic simulations with emphasis on the non-linear dynamics (oil whirl; oil whip) and their impact on bearing friction losses. In order to obtain a detailed understanding of these effects; elastic multibody simulations (EMBS) with elastic hydrodynamic bearing (EHD) analysis including a mass-conservative approach are used. Measurement data is obtained using a unique test bench which is designed to quantify the bearing friction losses by means of a drag test. Additionally, hot gas test bench measurements are carried out to assess the non-linear rotor dynamics during steady state operation using shaft motion measurement equipment. In the first step; a multibody simulation model of a common automotive TC is set up; and a model of the friction test bench is mapped into it. The author will show that there is a high agreement between simulated and measured friction losses. In the second step; the TC model is detached from the virtual test bench and a variation of the essential parameters are carried out to identify the influence of the non-linear rotor dynamics on the bearing friction. A final model validation is obtained by comparing the measured shaft orbits for the TC hot gas test bench with the results from the multibody simulation.