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Abstract Energy management strategies for charge-sustaining hybrid electric vehicles reduce fuel consumption and maintain battery pack state of charge while meeting driver output power demand. The equivalent consumption minimization strategy is a real-time energy management strategy that makes use of an equivalence ratio to quantify electric power consumption in terms of fuel power consumption. The magnitude of the equivalence ratio determines the hybrid electric vehicle mode of operation and influences the ability of the energy management strategy to reduce fuel consumption as well as maintain the battery pack state of charge. The equivalent consumption minimization strategy in this article uses three Willans line models, which have an associated marginal efficiency and constant offset, to model the performance in the hybrid electric vehicle controller.

Abstract This article presents surrogate mixtures that simulate the physical and chemical properties in the auto-ignition of hydrotreated vegetable oil (HVO). Experimental investigation was conducted in the Ignition Quality Tester (IQT) to validate the auto-ignition properties with respect to those of the target fuel. The surrogate development approach is assisted by artificial neural network (ANN) embedded in MATLAB optimization function. Aspen HYSYS is used to calculate the key physical and chemical properties of hundreds of mixtures of representative components, mainly alkanes—the dominant components of HVO, to train the learning algorithm. Binary and ternary mixtures are developed and validated in the IQT. The target properties include the derived cetane number (DCN), density, viscosity, surface tension, molecular weight, and volatility represented by the distillation curve. The developed surrogates match the target fuel in terms of ignition delay and DCN within 6% error range.

Abstract To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, “U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards” identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis.

Abstract Autonomy and connectivity are considered among the most promising technologies to improve safety and mobility and reduce fuel consumption and travel delay in transportation systems. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle while incorporating platoon formation and lane-changing decisions. We embed this trajectory planning model in a simulation framework to quantify its fuel efficiency and travel time reduction benefits for the subject vehicle in a dynamic traffic environment. Specifically, we compare and analyze the statistical performance of different controller designs in which lane changing or platooning may be enabled, under different values of time (VoTs) for travelers.

Abstract Environmental protection and the depletion of nonrenewable energy sources necessitate the search for the replacement of, among others, diesel fuel (Df) in diesel engines with renewable fuel without major structural changes. For this reason, vegetable oils are of interest as a possible fuel for this type of engine. Unfortunately, the physicochemical properties of vegetable oils differ significantly from Df. In addition to the boiling and freezing points, these properties include viscosity, density, and surface tension as well as wetting properties. For this reason, an attempt was made to modify these properties by adding n-hexane (Hex) and ethanol (Et) to canola oil (Co). The viscosity, density, surface tension, and wetting properties of Hex and Et are significantly different from those for Co.

Abstract Uncertainty of fuel reserves, environmental crisis, and health concerns arise from transport demands and reliance on fossil fuels. Downsized gasoline turbocharged direct injection (GTDI) engines have been developed and applied to most modern gasoline vehicles, delivering superior efficiency in high-load operation, reduced friction, and weight. But fuel enrichment and late combustion phasing to mitigate knocking combustion have hindered the efficiency benefits at higher loads with high boost. Furthermore, the wide valve-overlap with a three-cylinder setup for the maximum scavenging efficiency produces bursts of short-circuit (SC) air to cause underestimation of the equivalence ratio by the oxygen sensor, resulting in higher tailpipe nitrogen oxides (NOx) emissions with three-way catalyst (TWC) exhaust aftertreatment. Reducing the valve overlap to limit short-circuiting and enrichment will recover the combustion efficiency and the engine ER, but at the cost of high knock onset.

Abstract TOC

Abstract This study proposes a real-time control for plug-in hybrid electric vehicles (PHEVs) based on dynamic programming (DP). In order to obtain the optimal controls, DP is first used to solve the driving cycle, and a model-based calibration (MBC) tool is used to generate the optimal maps from the optimal trajectories. Further, a feedback energy management system (FEMS) is developed with the SoC as the feedback variable, which considers the charge and discharge reaction of the battery. To make full use of the energy stored in the battery, combined with the charge depletion-charge sustain (CDCS) strategy, the reference SoC is introduced. Finally, comparative simulation of the proposed real-time controller and DP is performed. The obtained results show that the fuel consumption of the real-time controller is 4.82 L/100 km in the worldwide harmonized light-duty vehicles’ test cycles, which is close to the fuel consumption with DP at 4.69 L/100 km.

Abstract The complex hydropneumatic electromagnetic coupling structure of the dual-fuel injector leads to its complicated injection process. The unknown problem of fuel injection characteristics limits the injector design and optimization process of combustion efficiency. Therefore, the scientific study of dual-fuel injection mechanism and online identification method is the key to grasping the diesel-gas coupled injection mechanism, and an important theoretical basis for advanced closed-loop control. In this study, an identification method for the time characteristics of the dual-fuel injector injection process is based on the injector inlet pressure, which can be applied to the diesel-natural gas co-direct injection engine. First, the cause and transfer process of diesel injection pressure waves were analyzed based on the Riemann invariant theory.

Abstract Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value.

Abstract Commercial vehicles require continual improvements in order to meet fuel consumption standards, improve diesel aftertreatment (AT) system performance, and optimize vehicle fuel economy. Simultaneous reductions in both CO2 and NOX emissions will be required to meet the upcoming regulatory targets for both EPA Phase 2 Greenhouse Gas Standards and new Low NOX Standards being proposed by the California Air Resources Board (CARB). In addition, CARB recently proposed a new certification cycle that will require high NOX conversion while vehicles are operating at lower loads than current regulatory cycles require. Cylinder deactivation (CDA) offers a powerful technology lever for meeting these two regulatory targets on commercial diesel engines. There have been numerous works in the past year showing the benefits of diesel CDA for elevating exhaust temperatures during low-load operation where it is normally too cold for AT to function at peak efficiency.

Abstract The airline industry faces a crisis in the future as consumer demand is increasing, but the environmental effects and depleting resources of kerosene mean that growth is unsustainable. Hydrogen is touted as the leading candidate to replace kerosene, but it needs significant technological and economical endeavors. In such a scenario, cryogenic liquid hydrogen (LH2) is predicted to be the most feasible method of using hydrogen. The major challenge of LH2 as an aircraft fuel is that it requires approximately four times the storage volume of kerosene—due to its lower density. Thus the design of cryogenic storage tanks to handle larger quantities of fuel is becoming increasingly important. But the increase in drag associated with larger storage tanks causes an increase in fuel consumption. Hence, this paper aims to evaluate the aerodynamic performance of different storage configurations and aid in the selection of an economic and efficient storage system.

Abstract This article investigates the ability of data-driven models to estimate instantaneous fuel consumption over 1 km road segments from different routes for different heavy-duty vehicles from the same fleet. Models are created using three different techniques: parametric, linear regression, and artificial neural networks. The proposed models use features derived from vehicle speed, mass, and road grade, which can be easily obtained from telematics devices, in addition to power take-off (PTO) active time, which is needed to capture the power requested by accessories in several heavy-duty vehicles. The robustness of these models with respect to the training data selection is improved by using k-fold cross-validation. Moreover, the inherent underestimation or overestimation bias of the model is calculated and used to offset the fuel consumption estimates for new routes. The study shows that the target application dictates the choice of model features.

Abstract In this article different one-dimensional (1D) models for spray penetration have been reviewed and investigated to confirm their validity. For each model, the underlying assumptions and the main equations were discussed in detail. A comparison between calculated data using these models and measured data showed that one specific semiempirical model exhibits the best agreement with the experimental data. Starting from this semiempirical model, a new model was derived based on the momentum theory. However, the calculated penetrations using both correlations appear to be very similar for diesel fuel (DF). Furthermore, experiments were carried out in a constant volume spray chamber in nonvaporizing conditions using DF and heavy fuel oil (HFO).

Abstract Federal Aviation Administration (FAA)’s 20 years of research and development with 200 unleaded blends and full-scale engine tests on 45 high-octane unleaded blends has not found a “drop-in” unleaded replacement for aviation gasoline (AVGAS) 100 low lead (100LL) fuel. In this study, analysis of compatibility via optimization of Lycoming O-320 engine fuelled with RON 97, RON 98, RON 100, and AVGAS was conducted using the Response Surface Methodology (RSM). Test fuels were compositionally characterized based on Gas Chromatography (GC) analysis and were categorized based on types of Hydrocarbon (HC). Basic fuel properties of fuels in this research were analyzed and recorded. For optimization analysis, engine speed and fuel were considered as the input parameters.

Abstract In order to meet the emission requirements of the China VI regulations on natural gas (NG) engines, the China VI compliant NG engines generally adopt the equivalent combustion technology route with high-pressure exhaust gas recirculation (HP-EGR). However, the HP-EGR introduction mode heavily relies on engine exhaust pressure, which has negative impact on engine pumping work. In regards to this issue, study on an alternative EGR technology is very important to achieve high EGR introduction ability with low pumping work. In this research, an experimental study on an equivalent-NG engine used in extended-range hybrid vehicles was carried out. The influence of high-low-pressure EGR (HLP-EGR) technology on engine combustion, performance, and emission characteristics was analyzed. The potential of HLP-EGR in improving engine economy and reducing emissions was explored.

Abstract In view of the traditional constant spacing policy (CSP) can’t maximize the fuel saving rate of the truck platoon when choosing the smaller desired vehicle spacing as the control target, a new control strategy is proposed in this article. This strategy dramatically reduces the fuel consumption of the truck platoon from the start to the formation of a stable platoon, thus greatly increasing the fuel saving rate of the platoon. To prove the effectiveness of the strategy, this article carried out the longitudinal dynamics modeling of the truck and the modeling of the fuel consumption model of engine first. Longitudinal dynamics modeling establishes the dynamic equations for truck braking and nonbraking. The fuel consumption model of engine is built using a three-dimensional map. Second, the design of the controller is described. The controller calculates the desired acceleration of the following vehicle based on the speed error and the following distance error.

Abstract The present work proposes a viable approach to develop single-cylinder diesel engines for the future by implementing regulated intake air boosting (RIAB) and engine downspeeding (ED) along with the well-established low compression ratio (LCR) approach. The investigations were conducted in a mass-production light-duty single-cylinder diesel engine initially equipped with a naturally aspirated (NA) intake system. By lowering the compression ratio (CR) and implementing the intake air boosting (IAB) using a belt-driven supercharger, the maximum brake mean effective pressure (BMEP) of the engine could be increased by 50%. More importantly, the improved performance could be achieved without violating the peak firing pressure (PFP) limits. However, a significant penalty was observed in the brake-specific fuel consumption (BSFC) at low-load operating points due to the additional power consumption of the IAB system.

Abstract Artificial intelligence-based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties. The present work deals with the development of an ANN model that can predict the derived cetane number (DCN) of oxygenated fuels containing alcohol and ether functionalities. Experimental DCNs of 499 fuels comprised of 116 pure compounds, 222 pure compound blends, and 159 real fuel blends were used as the dataset for model development. DCN measurements of sixty new fuels were carried out in the present work, and the data for the rest were collected from the literature.

Abstract Recently, the hybridization of the conventional fuel vehicle has attracted extensive attention among the automotive industry and related research institutions to meet increasingly rigorous fuel consumption (FC) regulations and emissions. This article introduces a hybridization design and parameter optimization methodology to transform a conventional fuel powertrain into the biaxial hybrid one. To utilize this hybrid powertrain, an energy management strategy (EMS) is proposed based on the rule-based control strategy which determines torque distribution between the engine and the motor according to the engine optimal FC area. To achieve better fuel economy, an off-line optimization of both control parameters and powertrain parameters is conducted using the multi-objective particle swarm optimization (MOPSO) algorithm. The research on the fuel economy potential of this hybrid powertrain, corresponding EMS, and parameters optimization are carried out through simulation.