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Abstract Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions.

Abstract The water droplet erosion (WDE) on high-speed rotating wheels appears in several engineering fields such as wind turbines, stationary steam turbines, fuel cell turbines, and turbochargers. The main reasons for this phenomenon are the high relative velocity difference between the colliding particles and the rotor, as well as the presence of inadequate material structure and surface parameters. One of the latest challenges in this area is the compressor wheels used in turbochargers, which has a speed up to 300,000 rpm and have typically been made of aluminum alloy for decades, to achieve the lowest possible rotor inertia. However, while in the past this component was only encountered with filtered air, nowadays, due to developments in compliance with tightening emission standards, various fluids also collide with the spinning blades, which can cause mechanical damage.

Abstract The two-branch exhaust of an asymmetric twin-scroll turbocharged engine are asymmetrically and periodically complicated, which has great impact on turbine matching. In this article, a matching effect of turbine speed parameter on asymmetric twin-scroll turbines based on the exhaust pulse energy weight distribution of a heavy-duty diesel engine was introduced. First, it was built as an asymmetric twin-scroll turbine matching based on exhaust pulse energy distribution. Then, by comparing the average matching point and energy matching points on the corresponding turbine performance map, it is revealed that the turbine speed parameter of energy matching points was a significant deviation from the turbine speed parameter under peak efficiency, which leads to the actual turbine operating efficiency lower than the optimal state.

Abstract Asymmetric twin-scroll turbocharging technology, as one of the effective technologies for balancing fuel economy and nitrogen oxide emissions, has been widely studied in the past decade. In response to the ever-increasing demands for improved fuel efficiency and reduced exhaust emissions, extensive research efforts have been dedicated to investigating various aspects of this technology. Researchers have conducted both experimental and simulation studies to delve into the intricate flow mechanism of asymmetric twin-scroll turbines. Furthermore, considerable attention has been given to exploring the optimal matching between asymmetric twin-scroll turbines and engines, as well as devising innovative flow control methods for these turbines. Additionally, researchers have sought to comprehend the impact of exhaust pulse flow on the performance of asymmetric twin-scroll turbines.

Abstract Reactivity-controlled compression ignition (RCCI) engine is an innovative dual-fuel strategy, which uses two fuels with different reactivity and physical properties to achieve low-temperature combustion, resulting in reduced emissions of oxides of nitrogen (NOx), particulate matter, and improved fuel efficiency at part-load engine operating conditions compared to conventional diesel engines. However, RCCI operation at high loads poses challenges due to the premixed nature of RCCI combustion. Furthermore, precise controls of indicated mean effective pressure (IMEP) and CA50 combustion phasing (crank angle corresponding to 50% of cumulative heat release) are crucial for drivability, fuel conversion efficiency, and combustion stability of an RCCI engine.

Abstract Methanol is emerging as an alternate internal combustion engine fuel. It is getting attention in countries such as China and India as an emerging transport fuel. Using methanol in spark ignition engines is easier and more economical than in compression ignition engines via the blending approach. M85 (85% v/v methanol and 15% v/v gasoline) is one of the preferred blends with the highest methanol concentration. However, its physicochemical properties significantly differ from gasoline, leading to challenges in operating existing vehicles. This experimental study addresses the challenges such as cold-start operation and poor throttle response of M85-fueled motorcycle using a port fuel injection engine. In this study, M85-fueled motorcycle prototype is developed with superior performance, similar/better drivability, and lower emissions than a gasoline-fueled port-fuel-injected motorcycle.

Abstract Pre-chamber jet ignition technologies have been garnering significant interest in the internal combustion engine field, given their potential to deliver shorter burn durations, increased combustion stability, and improved dilution tolerance. However, a clear understanding of the relationship between pre-chamber geometry, operating condition, jet formation, and engine performance in light-duty gasoline injection engines remains under-explored. Moreover, research specifically focusing on high dilution levels and passive pre-chambers with optical accessibility is notably scarce. This study serves to bridge these knowledge gaps by examining the influence of passive pre-chamber nozzle diameter and dilution level on jet formation and engine performance.

Abstract The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed.

Abstract The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen-based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This article introduces the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy-duty (HD) fuel cell electric commercial vehicles (FCEV).

Abstract Different platoon controls of connected automated vehicles have been studied to improve the entire fleet’s overall energy efficiency and driving safety. The platoons can be used during highway cruising to reduce unnecessary braking, shorten required headway, and thus improve traffic capacity and fuel economy. They can also be used in urban driving to improve traffic efficiency at intersections. However, there remain two problems that prevent the technology from achieving maximum benefit. First, the presence of human-driven vehicles will change the behavior of the fleet and platoon control of connected mixed traffic. Second, the communication uncertainties impose negative impacts on the dynamics of the platoon. A high-performance state predictor for surrounding vehicles can reduce the human-driven vehicle’s influence and help handle communication uncertainties better.

Abstract Quantifying exhaust gas composition and temperature in vehicles with internal combustion engines (ICEs) is crucial to understanding and reducing emissions during transient engine operation. This is particularly important before the catalytic converter system lights off (i.e., during cold start). Most commercially available gas analyzers and temperature sensors are far too slow to measure these quantities on the timescale of individual cylinder-firing events, thus faster sensors are needed. A two-color mid-infrared (MIR) laser absorption spectroscopy (LAS) sensor for gas temperature and carbon monoxide (CO) mole fraction was developed and applied to address this technology gap. Two quantum cascade lasers (QCLs) were fiber coupled into one single-mode fiber to facilitate optical access in the test vehicle exhaust. The QCLs were time-multiplexed in order to scan across two CO absorption transitions near 2013 and 2060 cm–1 at 15 kHz.

Abstract Exhaust emission standards for road vehicles require on-board diagnostics (OBD) of all comprehensive powertrain components (CCMs) impacting pollutant emissions. The legislation defines the generic malfunction criteria and pollutant threshold limits to trigger the component functional degradation. The electric drivetrain in xEV (more than one propulsion energy converter) applications substitutes or supports the internal combustion engine (ICE) operation with electric machine (EM) power. Malfunctions in the electric drivetrain will lead to an increase in ICE power demand. Hence, the electric drive system is classified as a comprehensive component in the OBD legislation. The regulation defines monitoring of the EM performance. The malfunctions that could prevent the EM(s) from properly operating emission control strategies, including any ICE control activation or electric drivetrain performance degradation, should be monitored by the OBD system.

Abstract Lithium-ion batteries are the most crucial component of hybrid electric vehicles (HEVs) with respect to cost and performance. In this article, a new energy management strategy (EMS) is developed that improves fuel efficiency (FE) and suppresses the degradation of the battery. A hybridized two-layer algorithm that combines multi-objective nonlinear model predictive control (NMPC) with a rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The RB-NMPC is designed to optimize the torque split between the engine and electric motors while maintaining the maximum and minimum constraints of each component. The proposed EMS is incorporated into control-oriented vehicle models, and their performances are analyzed for different driving cycles by comparing with RB, dynamic programming (DP), and NMPC.

Abstract Certain advanced driver assistance systems (ADAS) have the potential to boost energy efficiency in real-world scenarios. This article details a radar-based driver assistance scheme designed to minimize fuel consumption for a commercial vehicle by predictively optimizing braking and driving torque inputs while accommodating the driver’s demand. The workings of the proposed scheme are then assessed with a novel integration of the driver assistance functionality in randomized traffic microsimulation. Although standardized test procedures are intended to mimic urban and highway speed profiles for the purposes of evaluating fuel economy and emissions, they do not explicitly consider the interactions present in real-world driving between the ego vehicle equipped with ADAS and other vehicles in traffic. This article presents one approach to address the drawback of standardized test procedures for evaluating the fuel economy benefits of ADAS technologies.

Abstract In the United States (USA), transportation is the largest single source of greenhouse gas (GHG) emissions, representing 27% of total GHGs emitted in 2020. Eighty-three percent of these came from road transport, and 57% from light-duty vehicles (LDVs). Internal combustion engine (ICE) vehicles, which still form the bulk of the United States (US) fleet, struggle to meet climate change targets. Despite increasingly stringent regulatory mechanisms and technology improvements, only three US states have been able to reduce their transport emissions to the target of below 1990 levels. Fifteen states have made some headway to within 10% of their 1990 baseline. Largely, however, it appears that current strategies are not generating effective results. Current climate-change mitigation measures in road transport tend to be predominantly technological.

Abstract In Asian countries, small two-wheelers form a major share of the automobile segment and contribute significantly to carbon dioxide (CO2) emissions. Hybrid drives, though not widely applied in two-wheelers, can reduce fuel consumption and CO2 emissions. In this work three hybrid topologies, viz., P2 (electric motor placed between engine and transmission), P3 (electric motor placed between transmission and final drive), and power-split concepts (with planetary gear-train) have been modeled in Simulink, and their fuel consumption and emissions under the World Motorcycle Test Cycle (WMTC) have been evaluated. A physics-based model for the Continuously Variable Transmission (CVT) was used which is capable of predicting its transient characteristics. A map-based fuel consumption model and a Neural Network (NN)-based transient emission model were used for the engine.

Abstract Homogenous lean combustion in a direct-injection spark-ignition (DISI) engine is a promising pathway to achieve significantly improved fuel economy, making already competitive petrol engines even more attractive as a future powertrain option. This study aims to enhance the fundamental understanding of flame growth occurring in a DISI engine with varied charge equivalence ratios of 1.0 to 0.6 while keeping a low compression ratio of 10.5, a typical side-mounted injector, and early injected homogenous charge conditions. A new flame front vector analysis is performed using the flame image velocimetry (FIV) method applied to 100 cycles of high-speed flame movies with trackable contrast variations and pattern changes in the flame boundary. A spatial filtering method is used to decompose the bulk flow component and high-frequency flow component with the latter being interpreted as turbulence.

Abstract Engine thermal management (ETM) is a promising technology that allows the reduction of harmful emissions and fuel consumption when the internal combustion engine (ICE) is started from a cold state. The key technology for ETM is the decoupling of the cooling pump from the crankshaft and the actuation of the pump independently. In this article, an electric engine cooling pump has been designed through a novel experimentally based procedure and operated on a vehicle equipped with an advanced turbocharged gasoline engine, particularly interesting for its hybridization potential. In the first phase, a dedicated experimental campaign was conducted off board on an engine identical to the one equipped in the vehicle to assess the characteristics of the cooling circuit and the reference pump performances.

Abstract For hybrid electric vehicles (HEVs) to operate efficiently, the amount of power drawn from each power source must be optimally controlled in real time. Recent studies have shown that deep reinforcement learning (DRL) can effectively control the power sources in HEVs. However, model-free DRL relies on a large data set sampled from the system for improved performance, which can be very time consuming and resource intensive. To address this issue, a new DRL strategy is presented in this article where existing vehicle data is exploited to pretrain offline neural networks (NNs) and then the trained NNs are combined with an online DRL algorithm to explore new control policy to further improve the fuel economy. In this manner, it is expected that the controller can quickly learn how to control the system optimally rather than learning an optimal control policy by interacting with the vehicle model from zero initial knowledge.

Abstract In this study, a numerical investigation of different premixed gaseous injection strategies was performed to understand their impact on the scavenging and mixture formation of an air-fuel premixed pre-chamber with high exhaust gas recirculation (EGR) operations. EGR dilution is effective for reducing coolant heat loss, pumping work at throttled conditions, and mitigates knock at high-load conditions, thus increasing engine efficiency. To further extend the EGR limit of an air-fuel premixed pre-chamber engine, the effects of different injection strategies (including timing, duration, pressure, pre-chamber volume, and hardware) on the EGR level, trap efficiency, and parasitic loss were determined. Regardless of injection duration and upstream pressure, injecting too early not only increased the amount of the injected premixed gas leaking into the main chamber but also was inefficient in reducing the EGR level in the pre-chamber.