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This experimental work focuses on defining the detailed morphology of secondary emission products derived from the combustion of cerium (Ce) fuel-borne catalyst (FBC) doped with diesel fuel. Cerium is often used to promote the oxidation of diesel particulates collected in diesel aftertreatment systems, such as diesel particulate filters (DPFs). However, it is suspected that the secondary products could be emitted from the vehicle tailpipe without being effectively filtered by the aftertreatment systems. In this work, these secondary emissions were identified by means of a high-resolution transmission electron microscope (TEM), and their properties were examined in terms of morphology and chemistry. In preparation for fuel doping, a cerium-based aliphatic organic compound solution was mixed with a low-sulfur (110 ppm) diesel fuel at 50 ppm in terms of weight concentration.

As part of an ongoing vehicle benchmarking effort at Argonne National Laboratory, four different power-split HEVs were tested on a chassis dynamometer to analyze their operational behavior and understand the control strategy and its relationship to the individual features of the vehicles tested. The controls that select the way in which engine operation matches best engine efficiency load points appears to have evolved From the Gen 1 to the Gen 2 Toyota Prius. The Ford Escape HEV and Lexus RX400h were also analyzed by using similar methods, although the data are not as extensive as those for the Prius hybrids. Whereas the Escape HEV appeared to operate in a manner similar to that of the Gen 1 Prius, the RX400h (with its relatively large engine) loads the engine with excess battery charge to keep it operating at higher power levels - apparently to improve overall efficiency.

Detailed diesel particulates morphology, nanostructures, fractal geometry, and nitrogen oxides (NOx) emissions were analyzed for five different test fuels in a 1.7-L, common-rail direct-injection diesel engine. The accurately formulated fuels permit the effects of sulfur, paraffins, aromatics, and naphthene concentrations to be determined. A novel thermophoretic sampling technique was used to collect particulates immediately after the exhaust valves. The morphology and nanostructures of particulate samples were examined, imaged with a high-resolution transmission electron microscope (HRTEM), and quantitatively analyzed with customized digital image processing/data acquisition systems. The results show that the particle sizes and the total mass of particulate matter (PM) emissions correlate most strongly with the fuels' aromatics and sulfur content.

The physical and morphological properties of the particulate matter emitted from a 1.7-liter light-duty diesel engine were characterized by observing its evolution in size and fractal geometry along the exhaust system. A common-rail direct-injection diesel engine, the exhaust system of which was equipped with a turbocharger, EGR, and two oxidation catalysts, was powered with a California low-sulfur diesel fuel at various engine-operating conditions. A unique thermophoretic sampling system, a high-resolution transmission electron microscope (TEM), and customized image processing/data acquisition systems were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively. The measurements were carried out at four different positions along the exhaust pipe.

A thermophoretic particulate sampling device was used to investigate the detailed morphology and microstructure of diesel particulates at various engine-operating conditions. A 75 HP Caterpillar single-cylinder direct-injection diesel engine was operated to sample particulate matter from the high-temperature exhaust stream. The morphology and microstructure of the collected diesel particulates were analyzed using a high-resolution transmission electron microscope and subsequent image processing/data acquisition system. The analysis revealed that spherical primary particles were agglomerated together to form large aggregate clusters for most of engine speed and load conditions. Measured primary particle sizes ranged from 34.4 to 28.5 nm at various engine-operating conditions. The smaller primary particles observed at high engine-operating conditions were believed to be caused by particle oxidation at the high combustion temperature.

This paper presents the feasibility study of using the worldwide harmonized light vehicles test cycle (WLTC) for the fuel consumption certification of Chinese Light-duty (LD) vehicles. First, the key steps and the technical routes of the development process of WLTC are summarized. Second, the operation data of 3082 vehicles in 41 typical cities of China are collected throughout the year. Then, the characteristics of the acquisition data are compared with WLTC. Finally, the feasibility of using WLTC for fuel consumption certification of Chinese LD vehicles is analyzed in three aspects, includes operation characteristics, weighting factors and fuel consumption. The result shows that there is obvious difference between WLTC and Chinese reality, and WLTC is not suitable for the fuel consumption certification of Chinese LD vehicles.

Detailed characteristics of morphology, nanostructures, and sizes were analyzed for particulate matter (PM) emissions from low-temperature combustion (LTC) modes of a single-cylinder, light-duty diesel engine. The LTC engines have been widely studied in an effort to achieve high combustion efficiency and low exhaust emissions. Recent reports indicate that the number of nucleation mode particles increased in a broad engine operating range, which implies a negative impact on future PM emissions regulations in terms of the nanoparticle number. However, the size measurement of solid carbon particles by commercial instruments is indeed controversial due to the contribution of volatile organics to small nanoparticles. In this work, an LTC engine was operated with various biofuel blends, such as blends (B20) of soy bean oil (soy methyl ester, SME20) and palm oil (palm methyl ester, PME20), as well as an ultra-low-sulfur diesel fuel.

The detailed morphological properties of diesel particulate matter were analyzed along the exhaust system at various engine operating conditions (in a range of 1000 - 2500 rpm and 10 - 75 % loads of maximum torques). A 1.7-L turbocharged light-duty diesel engine was powered with California low-sulfur diesel fuel injected by a common-rail injection system, of which particulate emissions were controlled by an exhaust gas recirculation (EGR) system and two oxidation catalysts. A unique thermophoretic sampling system first developed for internal combustion engine research, a high-resolution transmission electron microscope (TEM), and a customized image processing/data acquisition system were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively.

The fatigue load spectrum of the physical proving ground is the necessary input for fatigue life analysis of vehicle parts and components. It is usually obtained by Road Load Data Acquisition (RLDA) and loads decomposition using multi-body dynamics tools. Virtual Proving Ground (VPG) methodology is gradually replacing this technical strategy. The belgian road is the typical event in durability test, in this paper, the flexible body and FTire model are applied to the vehicle multi-body dynamics model in order to improve the simulation accuracy. The result shows that all the wheel center force, shock absorber displacement and axial force acquired by VPG simulation have excellent correlation with real vehicle measured data. It is also proved that the virtual proving ground technology is a reliable and effective method to obtain the fatigue load spectrum in the early stage of development.

Expanding upon the authors’ previous work which utilized a GT-Power model of the Cooperative Fuels Research (CFR) engine under Research Octane Number (RON) conditions, this work defines the boundary conditions of the CFR engine under Motored Octane Number (MON) test conditions. The GT-Power model was validated against experimental CFR engine data for primary reference fuel (PRF) blends between 60 and 100 under standard MON conditions, defining the full range of interest of MON for gasoline-type fuels. The CFR engine model utilizes a predictive turbulent flame propagation sub-model, and a chemical kinetic solver for the end-gas chemistry. The validation was performed simultaneously for thermodynamic and chemical kinetic parameters to match in-cylinder pressure conditions, burn rate, and knock point prediction with experimental data, requiring only minor modifications to the flame propagation model from previous model iterations.

An increase in spark-ignition engine efficiency can be gained by increasing the engine compression ratio, which requires fuels with higher knock resistance. Oxygenated fuel components, such as methanol, ethanol, isopropanol, or iso-butanol, all have a Research Octane Number (RON) higher than 100. The octane numbers (ON) of fuels are rated on the CFR F1/F2 engine by comparing the knock intensity of a sample fuel relative to that of bracketing primary reference fuels (PRF). The PRFs are a binary blend of iso-octane, which is defined to an ON of 100, and n-heptane, which represents an ON of 0. Above 100 ON, the PRF scale continues by adding diluted tetraethyl lead (TEL) to iso-octane. However, TEL is banned from use in commercial gasoline because of its toxicity. The ASTM octane number test methods have a “Fit for Use” test that validate the CFR engine’s compliance with the octane testing method by verifying the defined ON of toluene standardization fuels (TSF).

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.

Life prediction is a major focus for a commercial fuel cell stack, especially applied in fuel cell electric vehicles (FCEV). This paper proposes a data driven fuel cell lifetime prediction model using particle swarm optimized back-propagation neural network (PSO-BPNN). For the prediction model PSO-BP, PSO algorithm is used to determine the optimal hyper parameters of BP neural network. In this paper, total voltage of fuel cell stack is employed to represent the health index of fuel cell. Then the proposed prediction model is validated by the aging data from PEMFC stack in FCEV at the actual road condition. The experimental results indicate that PSO-BP model can predict the voltage degradation of PEMFC stack at actual road condition precisely and has a higher prediction accuracy than BP model.