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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.

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.

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.

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.

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.

Elastokinematic parameters of the axle stiffness are one of the important effects for vehicle dynamics, which are usually considered in full-vehicle real-time models. In order to integrate such effects into real-time models, a multibody axle model is placed on the suspension test rig and is clamped at mounting points. Statically defined load cases are applied on the wheel, and finally, lookup tables are generated, which represent the elastokinematics for the real-time environment. In this case, the Body-In-White (BIW) is considered to be ideally stiff. However, the elasticity of BIW significantly influences the elastokinematics behavior as well and should be integrated into real-time models. The present paper introduces an efficient approach to integrate the BIW compliance effects into lookup tables in addition to the suspension stiffness under consideration of the Elastokinematics By Inertia Force method (EBIF method).

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

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.

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.

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.

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.

The efficient operation of powertrain test benches in research and development is strongly influenced by the state of “health” of the functional test object. Hence, the use of Early Damage Detection Systems (EDDS) with Unit Under Test (UUT) monitoring is becoming increasingly popular. An EDDS should primarily avoid total loss of the test object and ensure that damaged parts are not completely destroyed, and can still be inspected. Therefore, any abnormality from the standard test object behavior, such as an exceeding of predefined limits, must be recognized at an early testing time, and must lead to a shutdown of the test bench operation. With sensors mounted on the test object, it is possible to isolate the damage cause in the event of its detection. Advanced EDDS configurations also optimize the predefined limits by learning new shutdown values according to the test object behavior within a very short time.

Gasoline particulate filters (GPF) recently entered the market, and are already regarded a state-of-the-art solution for gasoline exhaust aftertreatment systems to enable EU6d-TEMP fulfilment and beyond. Especially for coated GPF applications, the prognosis of the emission conversion performance over lifetime poses an ambitious challenge, which significantly influences future catalyst diagnosis calibrations. The paper presents key-findings for the different GPF application variants. In the first part, experimental GPF ash loading results are presented. Ash accumulates as thin wall layers and short plugs, but does not penetrate into the wall. However, it suppresses deep bed filtration of soot, initially decreasing the soot-loaded backpressure. For the emission calibration, the non-linear backpressure development complicates the soot load monitoring, eventually leading to compromises between high safety against soot overloading and a low number of active regenerations.

To achieve global market and brand specific drivability characteristics as unique selling proposition for the increasing number of passenger car derivatives, an objectified evaluation approach for the drivability capabilities of the various cars is required. Thereto, it is necessary to evaluate the influence of different engine concepts in various complex and interlinked powertrain topologies during engine load change maneuvers based on physical criteria. Such an objectification approach enables frontloading of drivability related engineering tasks by the execution of drivability development and calibration work within vehicle subcomponent-specific closed-loop real-time co-simulation environments in early phases of a vehicle development program. So far, drivability functionalities could be developed and calibrated only towards the end of a vehicle development program, when test vehicles with a sufficient level of product maturity became available.

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.

In order to reduce engine out CO2 emissions, it is a main subject to find new alternative fuels from renewable sources. For identifying the specification of an optimized fuel for engine combustion, it is essential to understand the details of combustion and pollutant formation. For obtaining a better understanding of the flame behavior, dynamic structure large eddy simulations are a method of choice. In the investigation presented in this paper, an n-heptane spray flame is simulated under engine relevant conditions starting at a pressure of 50 bar and a temperature of 800 K. Measurements are conducted at a high-pressure vessel with the same conditions. Liquid penetration length is measured with Mie-Scatterlight, gaseous penetration length with Shadowgraphy and lift-off length as well as ignition delay with OH*-Radiation. In addition to these global high-speed measurement techniques, detailed spectroscopic laser measurements are conducted at the n-heptane flame.

The laminar burning velocity is an important parameter in various combustion models for engine simulations. With respect to computational time for computational fluid dynamics (CFD) and full system engine simulations, the calculation of laminar burning velocities using a detailed chemical mechanism can be replaced by incorporation of approximation formulas, based on rate-ratio asymptotics. In the present study, a work flow is developed to analyze the engine efficiency performance of spark ignition engines with respect to the laminar burning velocity as a fundamental fuel property. Firstly, methane is used as a fuel to assess practicability of the approach. The procedure is subsequently adopted for market type gasoline surrogates, RON95 and RON100. Detailed chemistry calculations are carried out for the three target fuels using existing state of the art mechanisms, the Aramco [Zhou et al., Proc. Combust. Inst., pp. 403-411, 2017] and the ITV RWTH mechanism [Cai et al., Combust.

In the last years the research activities in the field of lightweighting have been advancing rapidly. The introduction of innovative materials and manufacturing technologies has allowed significant weight reduction. Despite this, novel technologies and materials have not reached a wide distribution. The reasons for this are mainly high production costs and environmental impacts of manufacturing that do not compensate benefits during operation. The paper deals with the AffordabLe LIghtweight Automobiles AlliaNCE (ALLIANCE) project which has the goal of developing novel advanced automotive materials and production technologies, aiming at an average 25% weight reduction over 100 k units/year, at costs of <3 €/kg. The article is focussed on Work Package 1 (WP1) of the project, aimed at estimating the full attributes of innovative design solutions by assessing costs, energy demand and GWP over the entire vehicle Life Cycle (LC).

In this article, the development challenges of a fuel cell system are explained using the example of the BREEZE! fuel cell range extender (FC-REX) applied in an FEV Liiona. The FEV Liiona is a battery electric vehicle based on a Fiat 500 developed by FEV. The BREEZE! system is the first applied 30 kW low temperature polymer electrolyte membrane (LT PEM) fuel cell system in the subcompact vehicle class. Due to the highly integrated system approach and dry cathode operation, a compact design of the range extender module with a system power density of 0.45 kW/l can be achieved so that the vehicle interior including trunk remains completely usable. System development for fuel cells significantly influences performance, efficiency, package, durability, and required maintenance effort of a fuel cell electric powertrain. In order to ensure safe and reliable operation, the fuel cell system has to be supplied with sufficient amounts of air, hydrogen, and coolant flows.

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.