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Powertrain electrification is becoming increasingly common in the transportation sector to address the challenges of global warming and deteriorating air quality. This paper introduces a novel “Build Your Hybrid” approach to experience and test various hybrid powertrain concepts. This approach is applied to the light commercial vehicles (LCV) segment due to the attractive combination of a Diesel engine and a partly electrified powertrain. For this purpose, a demonstrator vehicle has been set up with a flexible P02 hybrid topology and a prototype Hybrid Control Unit (HCU). Based on user input, the HCU software modifies the control functions and simulation models to emulate different sub-topologies and levels of hybridization in the demonstrator vehicle. Three powertrain concepts are considered for LCVs: HV P2, 48V P2 and 48V P0 hybrid. Dedicated hybrid control strategies are developed to take full advantage of the synergies of the electrical system and reduce CO2 and NOx emissions.

This paper describes an approach to reduce development costs and time by frontloading of engineering tasks and even starting calibration tasks already in the early component conception phases of a vehicle development program. To realize this, the application of a consistent and parallel virtual development and calibration methodology is required. The interaction between vehicle subcomponents physically available and those only virtually available at that time, is achieved with the introduction of highly accurate real-time models on closed-loop co-simulation platforms (HiL-simulators) which provide the appropriate response of the hardware components. This paper presents results of a heterogeneous testing scenario containing a real internal combustion engine on a test facility and a purely virtual vehicle using two different automatic transmission calibration and hardware setups.

New 12 V/48 V power net architectures are potential solutions to close the gap between customer needs and legislative requirements. In order to exploit their potential, an increased effort is needed for functional implementation and hardware integration. Shifting of development tasks to earlier phases (frontloading) is a promising solution to streamline the development process and to increase the maturity level at early stages. This study shows the potential of the frontloading of development tasks by implementing a virtual 48 V mild hybridization in an engine-in-the-loop (EiL) setup. Advanced simulation technics like functional mock-up interface- (FMI) based co-simulation are utilized for the seamless integration of the real-time (RT) simulation models and allow a modular simulation framework as well as a decrease in development time.

The Environmental Protection Agency (EPA) requirement for 54.5mpg by 2025 to reduce greenhouse gases has pushed the industry to look for alternative fuels to run vehicles. Electricity is of those green energies that can help auto industry to achieve those strict requirements. However, the electric or hybrid-electric vehicles brought new challenges into science and engineering world including the Noise and Vibration issues which are usually tied up with both airborne and structural noises. The electromagnetic force plays a significant role in acoustic noise radiation in the electric motor which is an air-gap radial Maxwell force. This paper describes an innovative approach to model the physics of noise radiated by the electric motor.

Lean burn gasoline spark-ignition engines can support the reduction of CO2 emissions for future hybrid passenger cars. Very high efficiencies and very low NOx raw emissions can be achieved, if relative air/fuel ratios λ of 2 and above can be reached. The biggest challenge here is to assure a reliable ignition process and to enhance the fuel oxidation in order to achieve a short burn duration and a good combustion stability. This article aims at introducing an innovative combustion system fully optimized for ultra-lean operation and very high efficiency. Thereto, a new cylinder head concept has been realized with high peak firing pressure capability and with a low surface-to-volume ratio at high compression ratios. 1D and 3D simulations have been performed to optimize the compression ratio, charge motion and intake valve lift. Numerical calculations also supported the development of the ignition system.

Nowadays, collision avoidance systems (CAS) are an intensive research topic since the majority of all traffic accidents are collisions that are caused due to inattention or unadjusted driving behavior of the driver. Up to date prototypic CAS are based on on-board environmental sensors, such as camera or radar systems, that scan the vehicle's surrounding environment in order to assess the situation's hazardousness. The functionality of the used sensors under varying environmental conditions and the limited sensor covering area require an enormous effort to ensure a reliable detection of obstacles, and thus limit the application of the systems. In order to expand the operating field of such systems, a Galileo-based CAS will be developed within the project ‘Galileo above’ (application centre for ground based traffic).

Physics-based models in a closed-loop feedback control of a premixed charge compression ignition (PCCI) engine can improve the combustion efficiency and potentially reduce harmful NOx and soot emissions. A stand-alone multi-zone combustion model has been proposed in the literature using a physics-based mixing approach. The scalar dissipation rate emerged as the determining parameter in the model for mixing among different zones in the mixture fraction space. However, the calculation of the scalar dissipation rate depends on three approaches: three-dimensional computational fluid dynamics (3-D CFD) combustion simulations based on representative interactive flamelet (RIF) model, tabulation, or an empirical algebraic model of the scalar dissipation rate fitted for the given operating conditions of the engine. While the 3-D CFD approach provides accurate results, it is computationally too expensive to use the multi-zone model in closed-loop control.

Depleting fossil resources, a constantly rising energy demand and worries about greenhouse gas emissions force society to explore novel concepts for future mobile propulsion. In the context of biofuels, the identification of novel, sustainably producible, tailored molecules meeting the property specifications derived from advanced engine combustion concepts therefore constitutes a major objective. Due to the tremendous amount of possible molecular structures, solely experimental search strategies are infeasible and highlight the need for a computer-aided biofuel identification framework. To this end, a holistic approach for deriving truly predictive Quantitative-Structure-Property-Relationships (QSPRs) for engine-relevant fuel properties is presented. Such QSPRs are combined with a rigorous generation of molecular structures aiming at identification of single-compound fuel candidates for use in compression ignition (CI) engines.

New combustion processes require an understanding of the highly three-dimensional flow field to effectively decrease fuel consumption and pollutant emission. Due to the complex spatial character of the flow the knowledge of the development of the flow in an extended volume is necessary. Previous investigations were able to visualize the discrete three-dimensional flow field through multi-plane stereoscopic PIV. In this study, cycle resolved tomographic particle-image velocimetry measurement have been performed to obtain a fully resolved representation of the three-dimensional flow structures at each instant. The analysis is based on the measurements at 80°, 160°, and 240° after top dead center(atdc) such that the velocity distributions at the intake, the end of the intake, and the compression stroke at an engine speed of 1,500 rpm are discussed in detail.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

The role of NVH testing has evolved from a firefighting role and a period of exploration to a well defined standard test role in the product development and validation process. Integral to this process is robust engineering, which drives the need to execute many tests quickly, efficiently and accurately. This allows the NVH specialist to concentrate on interpretation of results and spend less time on the acquisition of data. As the volume of data grows, this creates the opportunity to data mine an NVH database to compare results from large sample sizes and focus on product variation. Today's NVH laboratory is accountable for producing high quality, consistent, timely, and cost effective test reports. The basic core of the test has to be easy to set up and execute for a novice, yet still allow for exploratory tests by specialists as necessary. The NVH laboratory is now subject to the same budgetary pressures and quality audits as other testing operations.

Fuels derived from biomass will most likely contain oxygen due to the high amount of hydrogen needed to remove oxygen in the production process. Today, alcohol fuels (e. g. ethanol) are well understood for spark ignition engines. The Institute for Combustion Engines at RWTH Aachen University carried out a fuel investigation program to explore the potential of alcohol fuels as candidates for future compression ignition engines to reduce engine-out emissions while maintaining engine efficiency and an acceptable noise level. The soot formation and oxidation process when using alcohol fuels in diesel engines is not yet sufficiently understood. Depending on the chain length, alcohol fuels vary in cetane number and boiling temperature. Decanol possesses a diesel-like cetane number and a boiling point in the range of the diesel boiling curve. Thus, decanol was selected as an alcohol representative to investigate the influence of the oxygen content of an alcohol on the combustion performance.

Faced with one of the greatest challenges of humanity – climate change – the European Union has set out a strategy to achieve climate neutrality by 2050 as part of the European Green Deal. To date, extensive research has been conducted on the CO2 life cycle analysis of mobile propulsion systems. However, achieving absolute net-zero CO2 emissions requires the adjustment of the relevant key performance indicators for the development of mobile propulsion systems. In this context, research is presented that examines the ecological and economic sustainability impacts of a hydrogen-fueled mild hybrid vehicle, a hydrogen-fueled 48V hybrid vehicle, a methanol-fueled 400V hybrid vehicle, a methanol-to-gasoline-fueled plug-in hybrid vehicle, a battery electric vehicle, and a fuel cell electric vehicle. For this purpose, a combined Life-Cycle Assessment (LCA) and Life-Cycle Cost Assessment was performed for the different propulsion concepts.

Targeted fuel blending is a known method to improve the performance of an automotive engine. Two candidates for a biofuel blend are the linear C8H18O isomers 1-octanol and di-n-butyl ether (DNBE). Both fuels feature an increased amount of oxygen that reduces soot emissions. However, physical properties of both fuels differ significantly and thus, a different type of spray mixing and combustion is expected: The low reactivity of 1-octanol causes a long ignition delay enabling a better mixture homogenization, but also causes HC and CO emissions. DNBE in contrary is highly volatile, has a short ignition time and thus can act as an ignition booster for 1-octanol without losing positive effects concerning emissions. In this work a spray study is performed for blends of 1-octanol and DNBE. Measurements are conducted under diesel-like engine conditions with an 8-hole piezo injector. High-speed Schlieren and Mie scattering techniques are used for spray visualizations.

The noise performance of fully electric vehicles is essential to ensure that they gain market acceptance. This can be a challenge for several reasons. Firstly, there is no masking from the internal combustion engine. Next, there is pressure to move to cost-efficient motor designs such as Switched Reluctance Motors, which have worse vibro-acoustic behaviour than their Permanent Magnet counterparts. Finally, power-dense, higher speed motors run closer fundamental frequency to the structural resonances of the system [1]. Experience has shown that this challenge is frequently not met. Reputable suppliers have designed and developed their “quiet” subsystems to state of the art levels, only to discover that the assembled E-powertrain is unacceptably noisy. The paper describes the process and arising results for the noise simulation of the complete powertrain.

Besides optical and haptic criteria, the interior noise especially influences the quality impression of a vehicle. Separately audible disturbing noises are usually perceived as inadequate product quality. As a result, the reduction of disturbing noise components is a key factor for the overall product quality. Since the acoustic optimization is a complex and time consuming process, the need for an analysis tool which identifies automatically disturbing engine noise components within the vehicle interior noise is high. For this reason, a novel analysis tool has been developed which extracts tonal and impulsive engine noise components from the overall engine noise, and evaluates the annoyance of each noticeable engine component automatically. In addition, each disturbing noise is allocated to the emitting engine component. It is then possible to listen to each engine component noise individually and synthesize a target noise by superimposing manually weighted component noises.

Prevailing automotive development focus shifts towards passenger-centric development of vehicle systems. Comparative to autonomous driving development, the challenge evolves to describe all relevant driving situations with the necessary information and context to be able to develop and optimize vehicle systems to actual driving situations. The situational description or scenario, i.e., context or ambiance in which a vehicle is located, represents a fundamental factor in consideration of system behavior and respective system optimization opportunities. The challenge to solve the respective automotive engineering problems for nonlinear multidimensional parameter spaces or mixed integer classification problems is to describe and limit the possible solution space by suitable methodologies. Conventional methods prove inadequate solution as they can only be applied with significant financial resources and engineering time efforts, as known by autonomous driving system development.

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.

Exhaust aftertreatment systems must function sufficiently over the full useful life of a vehicle. In Europe this is currently defined as 160.000 km. With the introduction of Euro 7 it is expected that the required mileage will be extended to 240.000 km. This will then be consistent with the US legislation. In order to quantify the emission impact of exhaust system degradation, an Euro 7 exhaust aftertreatment system is aged by different accelerated approaches: application of the Standard Bench Cycle, the ZDAKW cycle, a novel ash loading method and borderline aging. The results depict the impact of oil ash on the oxygen storage capacity. For tailpipe emissions, the maximum peak temperatures are the dominant aging factor. The cold start performance is effected by both, thermal degradation and ash accumulation. An evaluation of this emission increase requires appropriate benchmarks.

Ethanol currently remains the leading biofuel in the transportation sector, with special focus on spark ignition engines, as a pure as well as a blend component. In order to provide reliable numerical simulations of gasoline combustion processes under the influence of ethanol for modern engine research, it is mandatory to develop well validated detailed kinetic combustion models. One key parameter for the numerical simulation is the laminar burning velocity. Under the aspect of minimizing the general simulation effort for burning velocities, well-validated models have to be reduced. As a base kinetic mechanism for the reduction and optimisation process with respect to burning velocity calculations, a detailed model presented by Zhao et al. (Int. J. Chem. Kin. 40 (1) (2007) 1-18) is chosen. The model has been extensively validated against shock tube, rapid compression machine and burning velocity data. The detailed model consists of 55 species and 290 reactions.