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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.

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.

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.

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.

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.

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.

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.

Plug-In Hybrid Vehicles (PHEV) have been of significant importance recently to comply with future CO2 and pollutant emissions limit. However, performance of these vehicles is closely related to the energy management strategy (EMS) used to ensure minimum fuel consumption and maximize electric driving range. While conventional EMS concepts are developed to operate in wide range of scenarios, this approach could potentially compromise the fuel consumption benefit due to the omission of route and traffic information. With the advancements in the availability of real-time traffic, navigation and driving route information, the EMS can be further optimized to extract the complete potential of a PHEV. In this context, this paper presents application of predictive energy management (PEM) functionalities combined with information such as live traffic data to reduce the fuel consumption for a P1/P3 configuration PHEV vehicle.

In order to reduce pollutant and CO2 emissions and fulfill future legislative requirements, powertrain electrification is one of the key technologies. In this context, especially 48V technologies offer an attractive cost to CO2 reduction ratio. 48V mild hybrid powertrains greatly benefit from additional electric intake air compression (E-Charging) and direct torque assist by an electric machine (E-Boosting). Both systems significantly improve the transient engine behavior while reducing the low end torque drawbacks of extreme downsizing and downspeeding. Since E-Charging and E-Boosting have different characteristics concerning transient torque response and energy efficiency, application of model predictive control (MPC) is a particularly suitable method to improve the operating strategy of these functions. MPC requires fast running real-time capable models that are challenging to develop for systems with pronounced nonlinearities.

On the way to emission-free mobility, future fuels must be CO2 neutral. To achieve this, synthetic fuels are being developed. In order to better assess the effects of the new fuels on the engine process, simulation models are being developed that reproduce the chemical and physical properties of these fuels. In this paper, the fuel DMC+ is examined. DMC+ (a mixture of dimethyl carbonate (DMC) and methyl formate (MeFo) mainly, characterized by the lack of C-C Bonds and high oxygen content) offers advantages with regard to evaporation heat, demand of oxygen and knock resistance. Furthermore, its combustion is almost particle free. With the aid of modern 0D/1D simulation methods, an assessment of the potential of DMC+ can be made. It is shown that the simulative conversion of a state-of-the-art gasoline engine to DMC+ fuel offers advantages in terms of efficiency in many operating points even if the engine design is not altered.

If fuels that are more resistant to auto-ignition are injected near TDC in compression ignition engines, they ignite much later than diesel fuel and combustion occurs when the fuel and air have had more chance to mix. This helps to reduce NOX and smoke emissions at much lower injection pressures compared to a diesel fuel. However, PPCI (Partially Premixed Compression Ignition) operation also leads to higher CO and HC at low loads and higher heat release rates at high loads. These problems can be significantly alleviated by managing the mixing through injector design (e.g. nozzle size and centreline spray angle) and changing CR (Compression Ratio). This work describes results of running a single-cylinder diesel engine on fuel blends by using three different nozzle design (nozzle size: 0.13 mm and 0.17 mm, centreline spray angle: 153° and 120°) and two different CRs (15.9:1 and 18:1).

Future Diesel engines must meet extended requirements regarding air-fuel ratio, exhaust gas recirculation (EGR) capability, and tailored exhaust gas temperatures in the complete engine map to comply with the future pollutant emission standards. In this respect, parallel turbines combined with two separate exhaust manifolds have the potential to increase the exhaust gas temperature upstream of the exhaust aftertreatment system and reduce the catalyst light-off time. Furthermore, variable exhaust valve (EV) lifts enable new control strategies of the boosting system without additional actuators. Therefore, hardware robustness can be improved. This article focuses on the parallel-sequential boosting concept (PSBC) for a high-performance four-cylinder Diesel engine with separated exhaust manifolds combined with EV deactivation. One EV per cylinder is connected to one of the separated exhaust manifolds and, thus, connected to one of the turbines.

In order to thoroughly investigate and improve the path from biofuel production to combustion, the Cluster of Excellence “Tailor-Made Fuels from Biomass” was installed at RWTH Aachen University in 2007. Since then, a variety of fuel candidates have been investigated. In particular, 2-methyl tetrahydrofurane (2-MTHF) has shown excellent performance w.r.t. the particulate (PM) / NOx trade-off [1]. Unfortunately, the long ignition delay results in increased HC-, CO- and noise emissions. To overcome this problem, the addition of di-n-butylether (DNBE, CN ∼ 100) to 2-MTHF was analyzed. By blending these two in different volumetric shares, the effects of the different mixture formation and combustion characteristics, especially on the HC-, CO- and noise emissions, have been carefully analyzed. In addition, the overall emission performance has been compared to EN590 diesel.

A regular flex-fuel engine can operate on any blend of fuel between pure gasoline and E85. Flex-fuel engines have relatively low efficiency on E85 because the hardware is optimized for gasoline. If instead the engine is optimized for neat ethanol, the efficiency may be much higher, as demonstrated in this paper. The studied two-liter engine was modified with a much higher compression ratio than suitable for gasoline, two-stage turbocharging and direct injection with piezo-actuated outwards-opening injectors, a stratified combustion system and custom in-house control system. The research engine exhibited a wide-open throttle performance similar to that of a naturally aspirated v8, while offering a part-load efficiency comparable to a state-of-the-art two-liter naturally aspirated engine. NOx will be handled by a lean NOx trap. Combustion characteristics were compared between gasoline and neat ethanol.

Multiple-injection strategies are widely used in DI diesel engines. However, the interaction of the injection pulses is not yet fully understood. In this work, a split injection into a combustion vessel is studied by multiple optical imaging diagnostics. The vessel provides quiescent high-temperature, high-pressure ambient conditions. A common-rail injector which is equipped with a three-hole nozzle is used. The spray is visualized by Mie scattering. First and second stage of ignition are probed by formaldehyde laser-induced fluorescence (LIF) and OH* chemiluminescence imaging, respectively. In addition formation of soot is characterized by both laser-induced incandescence (LII) and natural luminosity imaging, showing that low-sooting conditions are established. These qualitative diagnostics yield ensemble-averaged, two-dimensional, time-resolved distributions of the corresponding quantities.