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To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

A PPCI-diffusion combustion strategy has shown the potential to achieve high efficiency, clean gasoline compression ignition (GCI) combustion across the full engine operating range. By conducting a 3-D CFD-led combustion system design campaign, this investigation was focused on developing a next generation (NextGen), step-lipped piston design concept in a 2.6L advanced light-duty GCI engine. Key geometric features of the NextGen piston bowl were parametrized and studied with customized spray targeting. A low lip positioning design with 128° spray targeting was found to provide the best performance. Fuel injection strategy optimization was performed at a full-load operating point (OP), 2000 rpm/24 bar closed-cycle IMEP (IMEPcc).

In the foreseeable future, the growing energy crisis and environmental pollution problem pose severe challenges to the automobile powertrains and exhaust systems. However, conventional optimization methods, including multi-dimensional computational fluid dynamics model and bench experiments, are very time-consuming or expensive. Adding the application of data-driven models to engine research and development has the potential to reduce computational costs or the number of in-depth experiments. This purpose of this study was to compare the performance of widely used artificial neural network (ANN) and random forest (RF) model for predicting the fuel consumption and engine-out emissions of a calibrated spark ignition (SI) engine for any given condition.

This research numerically investigated the combustion process and exhaust emissions from a light-duty Gasoline Compression Ignition (GCI) engine operating at low load as well as medium load conditions using a commercial computational fluid dynamic (CFD) software Converge. The fuel injection strategies and thermal boundary conditions effects were examined to produce locally stratified and globally lean partially premixed compression ignition (PPCI) combustion. The effects of fuel injection pressure, number of injections, and the quantity of fuel injected in each pulse were examined and optimized for emissions and fuel consumption (FC) under the design constraints of 180 bar peak cylinder pressure (PCP) and 10 bar/° CA maximum pressure rise rate (MPRR).

In this last decade, non-destructive X-ray measurement techniques have provided unique insights into the internal surface and flow characteristics of automotive injectors. This has in turn contributed to enhancing the accuracy of Computational Fluid Dynamics (CFD) models of these critical injection system components. By employing realistic injector geometries in CFD simulations, designers and modelers have identified ways to modify the injectors’ design to improve their performance. In recent work, the authors investigated the occurrence of cavitation in a heavy-duty multi-hole diesel injector operating with a high-volatility gasoline-like fuel for gasoline compression ignition applications. They proposed a comprehensive numerical study in which the original diesel injector design would be modified with the goal of suppressing the in-nozzle cavitation that occurs when gasoline fuels are used.

In recent years gasoline compression ignition (GCI) has been shown to offer an attractive combination of low criteria pollutants and high efficiency. However, enabling GCI across the full engine load map poses several challenges. At high load, the promotion of partial premixing of air and fuel is challenging due to the diminished ignition-delay characteristics at high temperatures, while under low load operations, maintaining combustion robustness is problematic due to the low reactivity of gasoline. Variable valve actuation (VVA) offers a means of addressing these challenges by providing flexibility in effective compression ratio. In this paper, the effects of VVA were studied at high loads in a prototype heavy-duty GCI engine using a gasoline research octane number (RON) 93 at a geometric compression ratio (CR) of 15.7. Both late intake valve closing (LIVC) and early intake valve closing (EIVC) strategies were analyzed as a measure to reduce the effective compression ratio.

Achieving robust ignitability for compression ignition of diesel engines at cold conditions is traditionally challenging due to insufficient fuel vaporization, heavy wall impingement, and thick wall films. Gasoline compression ignition (GCI) has shown the potential to offer an enhanced NOx-particulate matter tradeoff with diesel-like fuel efficiency, but it is unknown how the volatility and reactivity of the fuel will affect ignition under very cold conditions. Therefore, it is important to investigate the impact of fuel physical and chemical properties on ignition under pressures and temperatures relevant to practical engine operating conditions during cold weather. In this paper, 0-D and 3-D computational fluid dynamics (CFD) simulations of GCI combustion at cold conditions were performed.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Fuel stratification effects on the combustion and emissions behaviors for partially premixed compression ignition (PPCI) combustion of a high reactivity gasoline (research octane number of 80) was investigated using the third generation Gasoline Direct-Injection Compression Ignition (Gen3 GDCI) multi-cylinder engine. The PPCI combustion mode was achieved through a double injection strategy. The extent of in-cylinder fuel stratification was tailored by varying the start of second fuel injection timing (SOIsecond) while the first fuel injection event was held constant and occurred during the intake stroke. Based on the experimental results, three combustion characteristic zones were identified in terms of the SOIsecond - CA50 (crank angle at 50% cumulative heat release) relationship: (I) no response zone (HCCI-like combustion); (II) negative CA50 slope zone: (early PPCI mode); and (III) positive CA50 slope zone (late PPCI mode).

There is a strong evidence that the overrepresentation of teen drivers in motor vehicle crashes is mainly due to their poor hazard perception skills, i.e., they are unskilled at appropriately detecting and responding to roadway hazards. This study evaluates two cuing systems designed to help teens better understand their driving environment. Both systems use directional color-coding to represent different levels of proximity between one’s vehicle and outside agents. The first system provides an overview of the location of adjacent objects in a head-up display in front of the driver and relies on drivers’ focal vision (focal cuing system). The second system presents similar information, but in the drivers’ peripheral vision, by using ambient lights (peripheral cuing system). Both systems were retrofitted into a test vehicle (2014 Toyota Camry). A within-subject experiment was conducted at the University of Michigan Mcity test-track facility.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.

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).

Cyclic variability in internal combustion engines (ICEs) arises from multiple concurrent sources, many of which remain to be fully understood and controlled. This variability can, in turn, affect the behavior of the engine resulting in undesirable deviations from the expected operating conditions and performance. Shot-to-shot variation during the fuel injection process is strongly suspected of being a source of cyclic variability. This study focuses on the shot-to-shot variability of injector needle motion and its influence on the internal nozzle flow behavior using diesel fuel. High-speed x-ray imaging techniques have been used to extract high-resolution injector geometry images of the sac, orifices, and needle tip that allowed the true dynamics of the needle motion to emerge. These measurements showed high repeatability in the needle lift profile across multiple injection events, while the needle radial displacement was characterized by a much higher degree of randomness.

This research investigates the potential of gasoline compression ignition (GCI) to achieve low engine-out NOx emissions with high fuel efficiency in a heavy-duty diesel engine. The experimental work was conducted in a model year (MY) 2013 Cummins ISX15 heavy-duty diesel engine, covering a load range of 5 to 15 bar BMEP at 1375 rpm. The engine compression ratio (CR) was reduced from the production level of 18.9 to 15.7 without altering the combustion bowl design. In this work, four gasolines with research octane number (RON) ranging from 58 to 93 were studied. Overall, GCI operation resulted in enhanced premixed combustion, improved NOx-soot tradeoffs, and similar or moderately improved fuel efficiency compared to diesel combustion. A split fuel injection strategy was employed for the two lower reactivity gasolines (RON80 and RON93), while the RON60 and RON70 gasolines used a single fuel injection strategy.

The accurate modeling of fuel spray behavior under diesel engine conditions requires well-characterized boundary conditions. Among those conditions, the spray cone angle is important due to its impact on the spray mixing process, flame lift-off locations and subsequent soot formation. The spray cone angle is a highly dynamic variable, but existing correlations have been developed mainly for diesel fuels at quasi-steady state and relatively low injection pressures. The objective of this study was to develop spray cone angle correlations for both diesel and a light-end gasoline fuel over a wide range of diesel-engine operating conditions that are capable of capturing both the transient and quasi-steady state processes. Two important macroscopic characteristics of solid cone sprays, the spray cone angle and spray penetration, were measured using a single-hole heavy-duty injector using two fuels at diesel engine conditions in an optical constant volume vessel.

Toluene primary reference fuel (TPRF) (mixture of toluene, iso-octane and heptane) is a suitable surrogate to represent a wide spectrum of real fuels with varying octane sensitivity. Investigating different surrogates in engine simulations is a prerequisite to identify the best matching mixture. However, running 3D engine simulations using detailed models is currently impossible and reduction of detailed models is essential. This work presents an AramcoMech reduced kinetic model developed at King Abdullah University of Science and Technology (KAUST) for simulating complex TPRF surrogate blends. A semi-decoupling approach was used together with species and reaction lumping to obtain a reduced kinetic model. The model was widely validated against experimental data including shock tube ignition delay times and premixed laminar flame speeds. Finally, the model was utilized to simulate the combustion of a low reactivity gasoline fuel under partially premixed combustion conditions.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

In this study a detailed 1-D engine system model coupled with 3-D computational fluid dynamics (CFD) analysis was used to investigate the air system design requirements for a heavy duty diesel engine operating with low reactivity gasoline-like fuel (RON70) under partially premixed combustion (PPC) conditions. The production engine used as the baseline has a geometric compression ratio (CR) of 17.3 and the air system hardware consists of a 1-stage variable geometry turbine (VGT) with a high pressure exhaust gas recirculation (HP-EGR) loop. The analysis was conducted at six engine operating points selected from the heavy-duty supplemental emissions test (SET) cycle, i.e., A75, A100, B25, B50, B75, and C100. The engine-out NOx target was set at 1 g/hp-hr (1.34 g/kWh) to address a future hypothetical tailpipe NOx limit of 0.02 g/hp-hr (0.027 g/kWh) while an engine-out particulate matter (PM) target of 0.01 g/hp-hr (0.013 g/kWh) was selected to comply with existing EPA 2010 regulations.

The current study utilized 3-D computational fluid dynamics (CFD) combustion analysis to guide the development of a viable full load range combustion strategy in a light-duty gasoline compression ignition (GCI) engine. A higher reactivity gasoline that has a research octane number (RON) of 70 was used for the combustion strategy development. The engine has a geometric compression ratio of 14.5 with a piston bowl designed to accommodate different combustion strategies and injector spray patterns. Detailed combustion optimization was focused on 6 and 18 bar gross indicated mean effective pressure (IMEPg) at 1500 rpm through a Design of Experiments approach. Two different strategies were investigated: (a) a late triggering fuel injection with a wide spray angle (combustion strategy #1); and (b) an early triggering fuel injection with a narrow spray angle (combustion strategy #2).