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Lean combustion technologies show promise for improving engine efficiency and reducing emissions. Among these technologies, prechamber-assisted combustion (PCC) is established as a reliable option for achieving lean or ultra-lean combustion. In this study, the effect of engine speed on PCC was investigated in a naturally aspirated heavy-duty optical engine: a comparison has been made between analytical performances and optical flame behavior. Bottom view natural flame luminosity (NFL) imaging was used to observe the combustion process. The prechamber was fueled with methane, while the main chamber was fueled with methanol. The engine speed was varied at 1000, 1100, and 1200 revolutions per minute (rpm). The combustion in the prechamber is not affected by changes in engine speed. However, the heat release rate (HRR) in the main chamber changed from two distinct stages with a faster first stage to more gradual and merged stages as the engine speed increased.

The global need for de-carbonization and stringent emission regulations are pushing the current engine research toward alternative fuels. Previous studies have shown that the uHC, CO, and CO2 emissions are greatly reduced and brake thermal efficiency increases with an increase in hydrogen concentration in methane-hydrogen blends for the richer mixture compositions. However, the combustion suffers from high NOx emissions. While these trends are well established, there is limited information on a detailed optical study on the effect of air-excess ratio for different methane-hydrogen mixtures. In the present study, experimental investigations of different methane-hydrogen blends between 0 and 100% hydrogen concentration by volume for the air-excess ratio of 1, 1.4, 1.8, and 2.2 were conducted in a heavy-duty optical diesel engine converted to spark-ignition operation. The engine was equipped with a flat-shaped optical piston to allow bottom-view imaging of the combustion chamber.

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.

Typical automotive emission testing systems usually employ Flame Ionization Detection (FID) analyzers to measure unburned fuel species in the exhaust, but the technique is not suitable for engines operating on alcohol fuels. The FID method is not sensitive to measuring unburned alcohol fuels due to the presence of oxygen bonds in the fuel molecule. Other techniques, such as Fourier Transform Infrared (FTIR), can provide accurate unburned fuel measurements with alcohol fuel. However, these techniques are expensive and are less accessible compared to FID analyzers. In this study, the unburned fuel emissions from the engine exhaust were measured simultaneously with FID and FTIR analyzers, with the engine operating on pure alcohols, which are methanol, ethanol, and n-butanol. While most previous work focuses on stoichiometric air-fuel mixtures, a wide range of lean operating conditions between global-λ 1.6 to 2.8 will be tested in this study.

Lean combustion is one of the most applied methods to increase engine efficiency and maintain a good trade-off with engine emissions. The pre-chamber combustion (PCC) is one of the most promising combustion concepts to extend the lean operating limits of the engine. The Narrow throat pre-chamber has shown better lean limit extension compared to other ignition sources. The pre-chamber jets and the main-chamber combustion were studied in a Heavy-Duty optical engine using methane fuel. The tested conditions covered global excess air ratios (λ), between 1.9 to 2.3. The combustion process was recorded using three collection systems: (a) Natural Flame Luminosity (NFL) with a temporal resolution of 0.1 CAD; (b) OH* Chemiluminescence, and (c) CH* Chemiluminescence with a temporal resolution of 0.2 CAD for both. The propagating velocity of the reacting jets was studied using Combustion Image Velocimetry (CIV) based on bottom view images of the main chamber.

Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

This paper presents the design, implementation, and performance evaluation of a reduced complexity algorithm for a predictive control which is based on our previously published SAE paper (2021-01-0225) titled, “Model Predictive Control for Engine Thermal Management System.” That paper presented a model predictive control (MPC) design concept and demonstrated energy efficiency improvements by enabling engine pre-cooling based on GPS/Navigation data to recognize future vehicle speed limit and road grade in anticipation of high engine load demand. When compared to conventional control, the predictive control demonstrated considerable energy and fuel savings due to delayed timing of both knock mitigation and activation of radiator cooling fan during high engine load demand. However, this predictive control strategy is much more complicated due to its highly coupled nonlinear behavior.

Progressively stringent emission regulations and increasing regulatory demands on fuel economy have led to advanced combustion development. Low temperature combustion (LTC), specifically homogenous charge compression ignition (HCCI), is a promising technology for reducing exhaust emissions and improving efficiency. However, its operating range is limited to low load without boosting and EGR, due to low volumetric efficiency and high pressure rise rates. In addition, effectively controlling the combustion phasing is another challenge in realizing the associated combustion gains. In this work, advanced valve control mechanisms known as continuously variable valve duration (CVVD) and continuously variable valve timing (CVVT) were used for both intake and exhaust valvetrains to enable negative valve overlap (NVO) for trapping hot exhaust residuals and to promote multipoint simultaneous ignition.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

To elucidate the complex characteristics of pre-chamber combustion engines, the interaction of the hot gas jets initiated by an active narrow throated pre-chamber with lean premixed CH4/air in a heavy-duty engine was studied computationally. A twelve-hole KAUST proprietary pre-chamber geometry was investigated using CONVERGE software. The KAUST pre-chamber has an upper conical part with the spark plug, and fuel injector, followed by a straight narrow region called the throat and nozzles connecting the chambers. The simulations were run for an entire cycle, starting at the previous cycle's exhaust valve opening (EVO). The SAGE combustion model was used with the chemistry modeled using a reduced methane oxidation mechanism based on GRI Mech 3.0, which was validated against in-house OH chemiluminescence data from the optical engine experiments.

Pre-chamber combustion (PCC) has re-emerged in recent last years as a potential solution to help to decarbonize the transport sector with its improved engine efficiency as well as providing lower emissions. Research into the combustion process inside the pre-chamber is still a challenge due to the high pressure and temperatures, the geometrical restrictions, and the short combustion durations. Some fundamental studies in constant volume combustion chambers (CVCC) at low and medium working pressures have shown the complexity of the process and the influence of high pressures on the turbulence levels. In this study, the pre-chamber combustion process was investigated by combustion visualization in an optically-accessible pre-chamber under engine relevant conditions and linked with the jet emergence inside the main chamber. The pre-chamber geometry has a narrow-throat. The total nozzle area is distributed in two six-hole rows of nozzle holes.

The study showcases the strength of machine learning (ML) models in imitating the operation of an advanced engine concept - the gasoline compression ignition (GCI) - at low loads. The GCI engine is prone to exceeding the limits of criteria emissions at such loads, especially at the cold start when the catalyst is not activated. One proposition to accelerate catalyst light-off is using spark-ignition. This, however, adds an extra level of complexity in identifying an optimum operation point. The ML models can be a useful tool in guiding the engine calibration process. In this study, the ML models are trained on GCI engine experiments, covering different intake conditions, injection strategies, and spark settings. The models can predict seven engine performance parameters: fuel consumption, four engine-out emissions, exhaust temperature, and coefficient of variation (COV) in indicated mean effective pressure (IMEP).

The increasing demand for high-octane fuels is pushing the combustion research towards investigating new potential fuels and octane boosters. In addition to their high-octane, those additives should be environmentally friendly. In this study, the anti-knock properties of Dicyclopentadiene (DCPD) as an additive to primary reference fuels (PRF) and toluene primary reference fuels (TPRF) have been investigated. The Research octane number (RON) and Motor octane number (MON) were measured using Cooperative Fuels Research (CFR) engine for four different fuel blends; PRF 60 + 10% DCPD, PRF 60 + 20% DCPD, PRF 70 + 10% DCPD and TPRF 70 + 10% DCPD. In addition, homogenous charge compression ignition (HCCI) was also performed using the CFR engine to show the effect of DCPD on suppressing low temperature chemistry of reference fuels.

Drag reducing agents (DRAs) are extensively used to increase the capacity of pipelines to transport crude oils and finished products. The amount of DRA that can be used in gasoline is limited by the tendency of the high molecular weight DRAs to form engine deposits. The use of deposit control additives (DCAs) could help to mitigate this effect, enabling increased DRA treatment rates and improved pipeline capacity. A study has been undertaken to investigate the engine test response of these additives, and has suggested that higher DRA treat rates may be possible when accompanied by a deposit control additive to address increased intake valve deposits. Conversely, the effect on combustion chamber deposits is not clear and further studies would be required. Other engine related aspects such as intake valve deposit stick have also been investigated and under the conditions tested do not appear to be adversely affected by either the DRA or the deposit control additive.

Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each other in close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces greenhouse gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel.

Autonomy for multiple trucks to drive in a fixed-headway platoon formation is achieved by adding precision GPS and V2V communications to a conventional adaptive cruise control (ACC) system. The performance of the Cooperative ACC (CACC) system depends heavily on the reliability of the underlying V2V communications network. Using data recorded on precision-instrumented trucks at both ACM and NCAT test tracks, we provide an understanding of various effects on V2V network performance: Occlusions - non-line-of-sight (NLOS) between the Tx and Rx antenna may cause network signal loss. Rain - water droplets in the air may cause network signal degradation. Antenna position - antennas at higher elevation may have less ground clutter to deal with. RF interference - interference may cause network packet loss. GPS outage - outages caused by tree cover, tunnels, etc. may result in degraded performance. Road curvature - curves may affect antenna diversity.

The effects of intake pressure (Pin), start of injection (SOI), injection pressure (Pinj), injection split ratio (Qsplit), internal and external exhaust gas recirculation rates were varied to optimize several key parameters at a partially pre-mixed combustion low load/low speed condition using CFD. These include indicated specific fuel consumption (ISFC), combustion phasing (CA50), maximum rate of pressure rise (MRPR), maximum cylinder pressure (Pmax), indicated specific NOx (sNOx), indicated specific hydrocarbons (sHC) and Filter Smoke Number (FSN) emissions. Low-load point (6 bar indicated mean effective pressure (IMEP), 1500 revolutions per minute (RPM)) was selected where Pin varied between 1.25 and 1.5 bar, SOI between -100 and -10 crank angle degree (CAD) after top dead center (aTDC), Pinj between 100 and 200 bar, split ratio between 0 and 0.5, EGR between 0 and 45% and internal EGR measured by rebreathing valve height was varied between 0 and 4.5 mm.

The combustion system of a heavy-duty diesel engine operated in a gasoline compression ignition mode was optimized using a CFD-based response surface methodology and a machine learning genetic algorithm. One common dataset obtained from a CFD design of experiment campaign was used to construct response surfaces and train machine learning models. 128 designs were included in the campaign and were evaluated across three engine load conditions using the CONVERGE CFD solver. The design variables included piston bowl geometry, injector specifications, and swirl ratio, and the objective variables were fuel consumption, criteria emissions, and mechanical design constraints. In this study, the two approaches were extensively investigated and applied to a common dataset. The response surface-based approach utilized a combination of three modeling techniques to construct response surfaces to enhance the performance of predictions.

A predictive control method for the cooling system of an engine is developed in order to improve fuel efficiency through the use of vehicle onboard GPS/Navigation system. Conventionally, in an internal combustion engine cooling system, coolant temperature is controlled from predefined maps or models depending on the engine speed, accelerator pedal position, engine torque, and/or fueling rate at that instant. Due to the instantaneous decisions taken to change target coolant temperature, road gradient changes in terrain could cause engine under-cooling on a steep uphill or over-cooling when driving downhill. The paper presents the concept of predictive coolant temperature control strategy, utilizing GPS/Navigation data to recognize driving conditions by sensing vehicle position, speed limit, and road information like elevation and grade.