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The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

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.

Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

One of the main challenges in internal combustion engine design is the simultaneous reduction of all engine pollutants like carbon monoxide (CO), total unburned hydrocarbons (THC), nitrogen oxides (NOx), and soot. Low-temperature combustion (LTC) concepts for compression ignition (CI) engines, e.g., premixed charged compression ignition (PCCI), make use of pre-injections to create a partially homogenous mixture and achieve an emission reduction. However, they present challenges in the combustion control, with the usage of in-cylinder pressure sensors as feedback signal is insufficient to control heat release and pollutant emissions simultaneously. Thus, an additional sensor, such as an ion-current sensor, could provide further information on the combustion process and effectively enable clean and efficient PCCI operation.

Dedicated-EGR™ (D-EGR™) is a concept where the exhaust of one dedicated cylinder (D-Cyl) is routed into the intake thus producing EGR to be used by the whole engine. The D-Cyl operates rich of stochiometric which produces syngas that enhances the EGR stream permitting faster combustion and greater knock mitigation. Operating an engine using D-EGR improves the knock resistance which can permit a higher compression ratio (CR) thereby increasing efficiency. One challenge of traditional D-EGR is that the D-Cyl combustion becomes unstable operating with both rich and EGR dilute conditions. Therefore, the ‘Split Intake D-EGR’ concept seeks to resolve this problem by feeding fresh air to the D-Cyl, thus allowing even richer operation in the D-Cyl which further increases the H2 and CO yield thereby enhancing the efficiency benefits.

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.

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.

Simultaneous reduction of engine pollutants (e.g., CO, THC, NOx, and soot) is one of the main challenges in the development of new combustion systems. Low-temperature combustion (LTC) concepts in compression ignition (CI) engines like premixed charged compression ignition (PCCI) make use of pre-injections to create a partly homogenous mixture. In the PCCI combustion regime, a direct correlation between injection and pollutant formation is no longer present because of long ignition delay times. In LTC combustion systems, the in-cylinder pressure sensor is normally used to help the combustion control. However, to allow the control of PCCI engines, new sensor concepts are investigated to obtain additional information about the PCCI combustion for advanced controller structures. In LTC combustion systems like gasoline-controlled autoignition (GCAI) concepts, the application of ion current sensors enables additional monitoring of the combustion process with real-time capability.

Renewable fuels, such as bio- and e-fuels, are of great interest for the defossilization of the transport sector. Among these fuels, methanol represents a promising candidate for emission reduction and efficiency increase due to its very high knock resistance and its production pathway as e-fuel. In general, reliable simulation tools are mandatory for evaluating a specific fuel potential and optimizing combustion systems. In this work, a previously presented methodology (Esposito et al., Energies, 2020) has been refined and applied to a different engine and different fuels. Experimental data measured with a single cylinder engine (SCE) are used to validate RANS 3D-CFD simulations of gaseous engine-out emissions. The RANS 3D-CFD model has been used for operation with a toluene reference fuel (TRF) gasoline surrogate and methanol. Varying operating conditions with exhaust gas recirculation (EGR) and air dilution are considered for the two fuels.

The reduction of harmful emissions is a key challenge in fighting climate change and global warming. Besides battery electric vehicles (BEVs), improved engine efficiency and alternate fuels, such as e-fuels or biofuels, can improve the emission budget of the transportation sector. Pred ictive simulations can be utilized as these avoid relying on slow manufacturing processes and expensive experiments. As the properties of alternative fuels can change drastically compared to classical fuels, even engine parameters, such as the mass flow rate, need to be reevaluated and optimized. However, simulation frameworks often rely on mass flow rates as input quantity, and hence, a prediction is impossible. This paper gives accurate pressure-based boundary conditions for multiphase systems and focuses on equations of state (EOS) employed in homogeneous equilibrium models (HEMs). Additionally, a dual-density approach is introduced to correct modeling errors that are intrinsic to a particular EOS.

Large-eddy simulation (LES) is an important tool to understand and analyze sprays, such as those found in engines. Subfilter models are crucial for the accuracy of spray-LES, thereby signifying the importance of their development for predictive spray-LES. Recently, new subfilter models based on physics-informed generative adversarial networks (GANs) were developed, known as physics-informed enhanced super-resolution GANs (PIESRGANs). These models were successfully applied to the Spray A case defined by the Engine Combustion Network (ECN). This work presents technical details of this novel method, which are relevant for the modeling of spray combustion, and applies PIESRGANs to the ECN Spray C case. The results are validated against experimental data, and computational challenges and advantages are particularly emphasized compared to classical simulation approaches.

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.

The most challenging part of the engine combustion development is the reduction of pollutants (e.g. CO, THC, NOx, soot, etc.) and CO2 emissions. In order to achieve this goal, new combustion techniques are required, which enable a clean and efficient combustion. For compression ignition engines, combustion rate shaping, which manipulates the injected fuel mass to control the in-cylinder pressure trace and the combustion rate itself, turned out to be a promising opportunity. One possibility to enable this technology is the usage of specially developed rate shaping injectors, which can control the injection rate continuously. A feasible solution with series injectors is the usage of multiple injections to control the injection rate and, therefore, the combustion rate. For the control of the combustion profile, a detailed injector model is required for predicting the amount of injected fuel. Simplified 0D models can easily predict single injection rates with low deviation.

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms.

The goal of reducing the impact of road transportation on the environment can be reached by different approaches. The use of non-fossil synthetic fuels from renewable energy sources in the entire fleet of internal combustion engine vehicles is only one promising pathway to minimize the vehicle’s carbon footprint during the use phase. The steadily tightening emissions legislation confront the developers of future combustion engines with major challenges: Historically, the chemical and physical improvement of the combustion process, tail pipe emissions reduction and the development of optimized after-treatment systems were linked to improvements in fuel quality. In order to further decrease exhaust gas emissions, the optimization of the chemical composition of renewable fuels are a basic requirement.

Engine knock is limiting the efficiency of spark ignition engines and consequently further reduction of CO2 emissions. Thus, an combustion process simulation needs a well working knock model to take this phenomenon into account. As knocking events result from auto-ignitions, the basis of a knock model is the accurate modeling of the latter. For this, the introduced 0D/1D knock model calculates the Livengood-Wu integral to estimate the state of the pre-reactions of the unburnt mixture and considers the two-stage auto-ignition of gasoline fuels, which occurs at specific boundary conditions. The model presented in this publication is validated against measurement data of a single cylinder engine. For this purpose, more than 12 000 knocking working cycles are investigated, covering extensively varied operating conditions for a wide-ranging validation.

With Post Euro 6 emission standards in discussion, stricter particulate number (PN) targets as well as a decreased PN cut-off size from 23 to 10 nm are expected. Sub-23 nm particulates are considered particularly harmful to human health, but are not yet taken into account in the current vehicle certification process. Not considering sub-23 nm particulates during the development process could lead to significant additional efforts for Original Equipment Manufacturers (OEM) to comply with future Post Euro 6 PN emission limits. It is therefore essential to increase knowledge about the formation and filtration of particulates below 23 nm. In the present study, a holistic Gasoline Particulate Filter (GPF) characterization has been carried out on an engine test bench under varying boundary conditions and on a burner bench with a novel ash loading methodology.

To achieve the strict legislative restrictions for emissions from combustion engines, vast improvements in engine emissions and efficiency are required. Two major impacting factors for emissions and efficiency are the reliable generation of an effective mixture before ignition and a fast, stable combustion process. While the mixture of air and injected fuel is generated by highly three-dimensional, time-dependent flow phenomena during the intake and compression stroke, the turbulent flame propagation is directly affected by the turbulence level in the flow close to the advancing flame front. However, the flow field in the combustion chamber is highly turbulent and subject to cycle-to-cycle variations (CCV). To understand the fundamental mechanisms and interactions, 3D flow measurements with combined high spatial and temporal resolution are required.

This paper focusses on the supply conditions of a connecting rod bearing. Thereto, a novel simulation approach is presented, which is based on a transient 3D-CFD multiphase flow simulation including the ability of gas dissolution and diffusive mass transfer. The model determines the pressure behavior and the gas bubble development in the oil supply system of a connecting rod bearing. It allows to visualize the flow behavior and the existence of gas bubbles in order to get a detailed impression of the physical occurrences. The experimental results from Maaßen [5], where a big gas bubble is formed in the supply bore by gas cavitation, are confirmed and used for validation. Further the flow behavior of free air ratios is investigated. The paper concludes that the supply conditions of a connecting rod bearing are strongly influenced by the gas bubble in terms of the fluid composition and the volume flow rate at the connecting rod bearing inlet.