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In the context of the energy transition, CO2-neutral solutions are of enormous importance for all sectors, but especially for the mobility sector. Hydrogen as an energy carrier has therefore been the focus of research and development for some time. However, the development of hydrogen combustion engines is in many respects still in the conception phase. Automotive system providers and engineering companies in the field of software development and simulation are showing great interest in the topic. In a joint project with the industrial partners Robert Bosch GmbH and AVL Germany, combustion in a H2-DI-engine for use in light-duty vehicles was methodically investigated using the CFD tool AVL FIRE®. The collaboration between Robert Bosch GmbH and the Institute for Mobile Systems (IMS) at Otto von Guericke University Magdeburg has produced a model study in which model approaches for the combustion of hydrogen can be analyzed.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

A methodology has been developed for identifying the combination of battery characteristics which lead to least-cost electric vehicles. Battery interrelationships include specific power vs, specific energy, peak power vs. specific energy and DOD, cycle life vs. DOD, cost vs. specific energy and peak power, and volumetric and battery size effects. The method is illustrated for the “second car” mission assuming lead/acid batteries. Reductions in life-cycle costs associated with future battery research breakthroughs are estimated using a sensitivity technique. A research prioritization system is described.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

The emission of particulate matter from future GDI engines has to be optimized, to comply with more stringent emission standards such as EU6. Therefore, the mechanisms responsible for the formation of particles have to be analyzed in detail. The understanding of the in-cylinder processes, necessary for this purpose, can only be achieved by a complementary use of optically accessible single-cylinder engines as well as the numerical simulation. This however leads to great demands on the 3D flow simulation. In this paper the complete CFD approach, incorporating a detailed description of the entire underlying model chain is shown. Particularly the wall surface temperature and the temperature drop due to the interaction with liquid fuel spray were identified as important parameters influencing the spray-wall interaction and thus also the particulate emissions. Nevertheless, in conventional CFD models, the spray cooling cannot be captured because of an assumed constant wall temperature.

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Road vehicles can expend a significant amount of energy in undesirable vertical motions that are induced by road bumps, and much of that is dissipated in conventional shock absorbers as they dampen the vertical motions. Presented in this paper are some of the results of a study aimed at determining the effectiveness of efficiently transforming that energy into electrical power by using optimally designed regenerative electromagnetic shock absorbers. In turn, the electrical power can be used to recharge batteries or other efficient energy storage devices (e.g., flywheels) rather than be dissipated. The results of the study are encouraging - they suggest that a significant amount of the vertical motion energy can be recovered and stored.

Filtration of diesel and gasoline fuel in automotive applications is affected by many external and internal parameters, e.g. vibration, temperature, pressure, flow pulsation, and engine start-stop. Current test procedures for automotive fuel filters, proposed by most of the researchers and organizations including Society for Automotive Engineers (SAE) and International Organization for Standardization (ISO), do not apply the previously mentioned real-world-conditions. These operating conditions, which are typical for an automotive fueling system, have a significant effect on fuel filtration and need to be considered for the accurate assessment of the filter. This requires the development of improved testing procedures that will simulate the operating conditions in a fuel system as encountered in the real world.

In this work, an experimental and analysis methodology was developed to evaluate the preignition propensity of fuels and engine operating conditions in an SI engine. A heated glow plug was introduced into the combustion chamber to induce early propagating flames. As the temperature of the glowplug varied, both the fraction of cycles experiencing these early flames and the phasing of this combustion in the engine cycle varied. A statistical methodology for assigning a single-value to this complex behavior was developed and found to have very good repeatability. The effects of engine operating conditions and fuels were evaluated using this methodology. While this study is not directly studying the so-called stochastic preignition or low-speed preignition problem, it studies one aspect of that problem in a very controlled manner.

This work presents a simple fan model that is based on the actuator disk approximation, and the blade element and vortex theory of a propeller. A set of equations are derived that require as input the rotational speed of the fan, geometric fan data, and the lift and drag coefficients of the blades. These equations are solved iteratively to obtain the body forces generated by the fan in the axial and circumferential directions. These forces are used as momentum sources in a CFD code to simulate the effect of the fan in an underhood thermal management simulation. To validate this fan model, a fan experiment was simulated. The model was incorporated into the CFD code STAR-CD and predictions were generated for axial and circumferential air velocities at different radial positions and at different planes downstream of the fan. The agreement between experimental measurements and predictions is good.

Stricter emissions legislation and growing demands for lower fuel consumption require significant efforts to improve combustion efficiency while satisfying the emission quality demands. Controlled Homogeneous Charge Compression Ignition (HCCI) combined with boosted air systems on gasoline engines provides a particularly promising, yet challenging, approach. Naturally aspirated (NA) HCCI has already shown considerable potential in combustion efficiency gains. Nevertheless, since the volumetric efficiency is limited in the NA HCCI operation range due to the hot residuals required to ignite the mixture and slow down reaction kinetics, only part-load operation is feasible in this combustion mode. Considering the future gasoline engine market with growing potentials identified in downsized gasoline engines, it becomes necessary to investigate the synergies and challenges of controlled, boosted HCCI.

Due to the increasingly stricter emission legislation and growing demands for lower fuel consumption, there have been significant efforts to improve combustion efficiency while satisfying the emission requirements. Homogeneous Charge Compression Ignition (HCCI) combined with turbo/supercharging on gasoline engines provides a particularly promising and, at the same time, a challenging approach. Naturally aspirated (n.a.) HCCI has already shown a considerable potential of about 14% in the New European Driving Cycle (NEDC) compared with a conventional 4-cylinder 2.0 liter gasoline Port Fuel Injection (PFI) engine without any advanced valve-train technology. The HCCI n.a. operation range is air breathing limited due to the hot residuals required for the self-ignition and to slow down reaction kinetics, and therefore is limited to a part-load operation area.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

For several years there has been a great deal of effort made in researching ways to run a compression ignition engine with simultaneously high efficiency and low emissions. Recently much of this focus has been dedicated to using gasoline-like fuels that are more volatile and less reactive than conventional diesel fuel to allow the combustion to be more premixed. One of the key challenges to using fuels with such properties in a compression ignition engine is stable engine operation at low loads. This paper provides an analysis of how stable gasoline compression ignition (GCI) engine operation was achieved down to idle speed and load on a multi-cylinder compression ignition engine using only 87 anti-knock index (AKI) gasoline. The variables explored to extend stable engine operation to idle included: uncooled exhaust gas recirculation (EGR), injection timing, injection pressure, and injector nozzle geometry.

In order to meet future requirements for emission reduction and fuel economy a variety of concepts are available for gasoline engines. In the recent past new pathways have been found using alternative fuels and fuel combinations to establish cost optimized solutions. The presented concept for a SI-engine consists of combined injection of gasoline and hydrogen. A hydrogen enriched gas mixture is being injected additionally to gasoline into the engine manifold. The gas composition represents the output of an onboard gasoline reformer. The simulations and measurements show substantial benefits to improve the combustion process resulting in reduced cold start and warm up emissions and optimized part load operation. The replacement of gasoline by hydrogen-rich gas during engine start leads to zero hydrocarbons in the exhaust gas.

This paper determines the impact of ambient temperature on energy consumption of a variety of vehicles in the laboratory. Several conventional vehicles, several hybrid electric vehicles, a plug-in hybrid electric vehicle and a battery electric vehicle were tested for fuel and energy consumption under test cell conditions of 20°F, 72°F and 95°F with 850 W/m₂ of emulated radiant solar energy on the UDDS, HWFET and US06 drive cycles. At 20°F, the energy consumption increase compared to 72°F ranges from 2% to 100%. The largest increases in energy consumption occur during a cold start, when the powertrain losses are highest, but once the powertrains reach their operating temperatures, the energy consumption increases are decreased. At 95°F, the energy consumption increase ranges from 2% to 70%, and these increases are due to the extra energy required to run the air-conditioning system to maintain 72°F cabin temperatures.

A methodology for evaluating life cycle cost of electric vehicles (EVs) to their buyers is presented. The methodology is based on an analysis of conventional vehicle costs, costs of drivetrain and auxiliary components unique to EVs, and battery costs. The conventional vehicle's costs are allocated to such subsystems as body, chassis, and powertrain. In electric vehicles, an electric drive is substituted for the conventional powertrain. The current status of the electric drive components and battery costs is evaluated. Battery costs are estimated by evaluating the material requirements and production costs at different production levels; battery costs are also collected from other sources. Costs of auxiliary components, such as those for heating and cooling the passenger compartment, are also estimated. Here, the methodology is applied to two vehicle types: subcompact car and minivan.