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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.

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.

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.

Crankshaft bearings function to maintain the lubrication oil films needed to support crankshaft journals in hydrodynamic regime of rotation. Discontinuous oil films will cause the journal-bearing couple to be in a mixed or boundary lubrication condition, or even a bearing seizure or a spun bearing. This condition may further force the crankshaft to break and an engine shutdown. Spun bearings have been identified to be one of the top reasons in field returned engines. Excessive investigations have found large, embedded hard debris particles on the bearings are inevitably the culprit of destroying continuity of the oil films. Those particles, in particular the suspicious steel residues, in the sizes of hundreds of micrometers, are large enough to cause oil film to break, but rather fine and challenging for materials engineers to characterize their metallurgical features. This article presents the methodology and steps of debris analyses on bearings at different stages of engine build.

In this paper, we present a novel algorithm designed to accurately trigger the engine coolant flow at the optimal moment, thereby safeguarding gas-engines from catastrophic failures such as engine boil. To achieve this objective, we derive models for crucial temperatures within a gas-engine, including the engine combustion wall temperature, engine coolant-out temperature, engine block temperature, and engine oil temperature. To overcome the challenge of measuring hard-to-measure signals such as engine combustion gas temperature, we propose the use of new intermediate parameters. Our approach utilizes a lumped parameter concept with a mean-value approach, enabling precise temperature prediction and rapid simulation. The proposed engine thermal model is capable of estimating temperatures under various conditions, including steady-state or transient engine performance, without the need for extra sensors.

The study focuses on understanding the air and oil flow characteristics within a ball bearing during high-speed rotation, with a particular emphasis on optimizing frictional heat dissipation and oil lubrication methods. Computational fluid dynamics (CFD) techniques are employed to analyze the intricate three-dimensional airflow and oil flow patterns induced by the motion of rotating and orbiting balls within the bearing. A significant challenge in conducting three-dimensional CFD studies lies in effectively resolving the extremely thin gaps existing between the balls, races, and cages within the bearing assembly. In this research, we adopt the ball-bearing structured meshing strategy offered by Simerics-MP+ to meticulously address these micron-level clearances, while also accommodating the rolling and rotation of individual balls. Furthermore, we investigate the impact of different designs of the lubrication ports to channel oil to other locations compared to the ball bearings.

Effective design of the lubrication path greatly influences the durability of any transmission system. However, it is experimentally impossible to estimate the internal distribution of the automotive transmission fluid (ATF) to different parts of the transmission system due to its structural complexities. Hybrid vehicle transmission systems usually consist of different types of bearings (ball bearings, thrust bearings, roller bearings, etc.) in conjunction with gear systems. It is a perennial challenge to computationally simulate such complicated rotating systems. Hence, one-dimensional models have been the state of the art for designing these intricate transmission systems. Though quantifiable, the 1D models still rely heavily on some testing data. Furthermore, HEVs (hybrid electric vehicles) desire a more efficient lubrication system compared to their counterparts (Internal combustion engine vehicles) to extend the range of operation on a single charge.

De-centralized brake actuation – that is, brake systems that incorporate individual actuators at each wheel brake location to both provide the apply energy and the modulation of braking force – is not a new area of study. Typically realized in the form of electro-mechanical brake calipers or drum brakes, or as “single corner” hydraulic actuators, de-centralized actuation in braking systems has already been deployed in production on General Motor EV1 Electric Vehicle (1997) in the form of electric drum brakes and has been studied continually by the automotive industry since then. It is frequently confused with “brake by wire,” and indeed practical implementations of de-centralized actuation are a form of brake by wire technology. However, with millions of vehicles on the road already with “brake by wire” systems - the vast majority of which have centralized brake actuation – the future of “brake by wire” is arguable settled.

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.

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.

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.

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.

Given their high-power density, large range of speed change, and reputation of being quieter than counter-shaft gear sets, planetary gear sets (PGS) have advantages to be applied in electric vehicle (EV) applications. Since electric drive unit (EDU) designs are often subject to accelerated development timelines with more versatile gear set layouts than conventional automotive transmissions, accurate prediction of PGS load sharing is needed. In the past, PGS load sharing imbalance used to be considered as a gear set problem focusing only on the effect to gear performance. Finding a closed-form formula has been a focus in gear design. However, early bearing failure in wind turbine gearboxes exposed the limitation of this strategy. With extensive field and laboratory testing, engineers started to notice that load sharing imbalance is essentially a system issue. Non-torque loads on PGS should be considered in the estimation by a gearbox system model.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

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.

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.