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The paper presents a robust adaptive control technique for precise regulation of a port fuel injection + direct injection (PFI+DI) system, a dual fuel injection configuration adopted in modern gasoline engines to boost performance, fuel efficiency, and emission reduction. Addressing parametric uncertainties on the actuators, inherent in complex fuel injection systems, the proposed approach utilizes an indirect model reference adaptive control scheme. To accommodate the increased control complexity in PFI+DI and the presence of additional uncertainties, a nonlinear plant model is employed, incorporating dynamics of the exhaust burned gas fraction. The primary objective is to optimize engine performance while minimizing fuel consumption and emissions in the presence of uncertainties. Stability and tracking performance of the adaptive controller are evaluated to ensure safe and reliable system operation under various conditions.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

A linear parameter-varying model predictive control (LPVMPC) is proposed to enhance the longitudinal vehicle speed control of a gas-engine vehicle, with potential application in autonomous vehicles. To achieve this objective, an advanced vehicle dynamic model and a sophisticated fuel consumption model are derived, forming a control-oriented model for the proposed control system. The vehicle dynamic model accurately captures the motions of the tires and the vehicle body. The fuel consumption model incorporates new powertrain modes such as automatic engine stop/start, active fuel management, and deceleration fuel cut-off, etc. The performance of the proposed LPV-MPC is evaluated by comparing it to a PID controller. Both simulation tests and vehicle-in-the-loop tests demonstrate the superior performance of the proposed controller. The results indicate that the LPV-MPC provides improved longitudinal vehicle speed control and reduced fuel consumption.

A gasoline’s distillation profile is directly related to its hydrocarbon composition and the volatility (boiling points) of those hydrocarbons. Generally, the volatility profiles of U.S. market fuels are characterized using a very simple, low theoretical plate distillation separation, detailed in the ASTM D86 test method. Because of the physical chemistry properties of some compounds in gasoline, this simple still or retort distillation has some limitations: separating azeotropes, isomers, and heavier hydrocarbons. Chemists generally rely on chromatographic separations when more detailed and precise results are needed. High-boiling aromatic compounds are the primary source of particulate emissions from spark ignited (SI), internal combustion engines (ICE), hence a detailed understanding and high-resolution separation of these heavy compounds is needed.

Methanol is currently being evaluated as a promising alternative fuel for internal combustion engines, due to being attainable by carbon neutral or negative pathways (renewable energy and carbon capture technology). The low ignitability of methanol has made it attractive mostly as a fuel for spark ignition engines, however the low sooting properties of the fuel could potentially reduce the NOx-soot tradeoff present in compression ignition engines. In this work, using a 4-cylinder engine with compression ratio modified from 16:1 to 19:1, methanol combustion is evaluated under five operating conditions in terms of fuel consumption, criteria pollutants, CO2 emissions and engine efficiency in addition to the qualitative assessment of the combustion stability. It was found that combustion is stable at medium to high loads, with medium load NOx emissions levels at least 30% lower than the original diesel engine and comparable emissions at maximum load conditions.

Heating devices are effective technologies to strengthen emission robustness of AfterTreatment Systems (ATS) and to guarantee emission compliance in the new boundaries given by upcoming legislations. Moreover, they allow to manage the ATS warm-up independently from engine operating conditions, thereby reducing the need for specific combustion strategies. Within heating devices, an attractive solution to provide the required thermal power without mandating a 48V platform is the fuel burner. In this work, a model-based control coordinator to manage the interaction between engine, ATS and fuel burner device has been developed, virtually validated, and optimized. The control function features a burner model and a control logic to deliver the needed amount of thermal energy, while ensuring ATS hardware protection.

The energy management strategy, commonly known as the EMS, is an essential component of fuel cell cars (FCVs). The majority of current research is concentrated on centralized emergency management systems (Cen-EMSs), but it does not provide sufficient flexibility (plug-and-play) or robustness. Regarding this matter, a hierarchical decentralized energy management system (Dec-EMS) that is based on a model predictive control (MPC) technique is offered for a modular FCV powertrain that is comprised of two parallel proton exchange membrane fuel cells (PEMFC) and an energy storage system. Gain scheduling makes the proposed Dec-EMS controller more effective in terms of its performance. The hierarchical decentralized control approach is assessed within the framework of a driving scenario that is representative of real-world conditions. According to the numerical result, the decentralized emergency management system (Dec-EMS) proposal provides superior performance than the centralized approach.

As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network.

The power loss of bearings is a significant factor in the overall efficiency in a drive unit system. Such bearings are subject to combined radial and axial loading needed to support the gear mesh forces. An experimental methodology has been developed to perform sets of power loss measurements on TRB, 4PCBB and DGBB. These measurements were performed under a variety of speed, load, temperature, and lubrication conditions. The loss behaviors of these types of the bearings are discussed, along with the tradeoff of different bearing arrangements for the fuel economy cycles. Several power loss models are employed to assess the accuracy of the estimations as compared to the experimental measurements. At low speed some models showed good correlations for TRB and DGBB, while at higher speed, they start deviating from the testing results. A higher fidelity model for estimating the losses at high speed, especially speed around 20krpm and beyond, needs to be developed.

Prior work in the literature have shown that pre-chamber spark plug technologies can provide remarkable improvements in engine performance. In this work, three passively fueled pre-chamber spark plugs with different pre-chamber geometries were investigated using in-cylinder high-speed imaging of spectral emission in the visible wavelength region in a single-cylinder direct-injection spark-ignition gasoline engine. The effects of the pre-chamber spark plugs on flame development were analyzed by comparing the flame progress between the pre-chamber spark plugs and with the results from a conventional spark plug. The engine was operated at fixed conditions (relevant to federal test procedures) with a constant speed of 1500 revolutions per minute with a coolant temperature of 90 oC and stoichiometric fuel-to-air ratio. The in-cylinder images were captured with a color high-speed camera through an optical insert in the piston crown.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time.

Due to traffic congestion and environmental pollution, connected automated vehicle (CAV) technologies based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure communication (V2I) have gained increasing attention from both academia and industry. Connected hybrid electric vehicles (CHEVs) offer great opportunities to reduce vehicular operating costs and emissions. However, in complex traffic scenarios, high-quality real-time energy management of CHEVs remains a technical challenge. To address the challenge, this paper proposes a hierarchical eco-driving strategy that consists of speed planning and energy management layers. At the upper layer, by leveraging the real-time traffic data provided by vehicle-to-everything (V2X) communication, dynamic traffic constraints are predicted by the traffic flow predictor developed based on the Hankel dynamic mode decomposition algorithm (H-DMD).

Stochastic Preignition (SPI) is an abnormal combustion phenomenon for internal combustion engines (ICE), which has been a significant impact to automotive companies developing high efficiency, turbocharged, direct fuel injection, spark ignited engines. It is becoming clearer what fuel properties are related to the cause of SPI, whether directly with fuel preparation in the cylinder, or mechanisms related to the deposit build-up which contributes to initial and follow-on SPI events. The purpose of this paper is to provide a summary of global market gasoline fuel properties with special attention given to properties and specific compounds from the fuel and fuel additives that can contribute to SPI and the deposit build-up in engines. Based on a review of the global fuel quality, it appears that the fuel quality has not caught up to meet the technology requirements for fuel economy from modern technology engines.

De-fossilization is an increasingly important trend in the energy sector. In the transport sector the de-fossilization efforts have been centered in promoting the electrification of vehicles, nonetheless other pathways, like the use of carbon neutral or carbon-offsetting fuels under current vehicle fleets, are also worth considering. Low-carbon fuels (LCF) can be synthetized from sources that can take advantage of the carbon already present in the atmosphere (either by technologies like direct carbon capture or biological processes like photosynthesis in biofuels) and use energy from renewable sources for the necessary industrial processes. Although, LCFs can be compared to fossil fuels as energy sources for internal combustion engines, their composition is not the same and their properties can modify the engine combustion and emissions.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

A computational study based on unsteady Reynolds-Averaged-Navier-Stokes that resolves the gas-liquid interface was performed to examine the unsteady multiphase flow in a 4 cylinder Inline (i-4) engine. In this study, the rotating motion of the crankshaft and reciprocating motion of the pistons were accounted for to accurately predict the oil distribution in various parts of the engine. Three rotational speeds of the crankshaft have been examined: 1000, 2800, and 4000 rpm. Of particular interest is to examine the mechanisms governing the process of oil drawdown from the engine head into the case. The oil distributions in other parts of the engine have also been investigated to understand the overall crankcase breathing process. Results obtained show the drawdown of oil from the head into the case to be strongly dependent on the venting strategy for the foul air going out of the engine through the PCV system.

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.

Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.

Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro6 and China6 standard. While providing an effective filtration of particles, the GPF increases the engine backpressure as a penalty due to accumulation of soot. To clean the accumulated soot, periodical burning of soot is achieved by the onboard control models and lot of effort is spent on calibrating the same. In order to understand pressure drop behavior across GPF, detailed pressure drop measurements were conducted at clean, soot and ash loaded conditions at engine dynamometer and at vehicle conditions. Effect of degreening of GPF was studied to take into account any change in pressure drop characteristics of onboard control models during GPF aging in the vehicle.

China's auto market has developed rapidly in recent years and has become the world's largest auto market. The rapid increase in sales of passenger cars has brought a series of environmental and energy problems. In response to these problems, “The Parallel Management Method for Corporate Average Fuel Consumption and New Energy Vehicles Credits” (Dual-credit Regulation) has been enacted in 2018. However, some problems about the regulation were gradually exposed with the NEV subsidies decreasing, such as too much surplus new energy vehicle credit. To promote the development of NEV, the reform of dual credit regulation was issued in June,2020.