Search Results

Abstract The transportation sector’s growing focus on addressing environmental and sustainable energy concerns has led to a pursuit of the decarbonization path. In this context, hydrogen emerges as a promising zero-carbon fuel. The ability of hydrogen fuel to provide reliable performance while reducing environmental impact makes it crucial in the quest for net zero targets. This study compares gasoline and hydrogen combustion in a single-cylinder boosted direct injection (DI) spark ignition engine under various operating conditions. Initially, the engine was run over a wide range of lambda values to determine the optimal operating point for hydrogen and demonstrate lean hydrogen combustion’s benefits over gasoline combustion. Furthermore, a load sweep test was conducted at 2000 rpm, and the performance and emission results were compared between gasoline and optimized hydrogen combustion. An in-depth analysis was conducted by varying fuel injection time and pressure.

Abstract This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions.

Abstract With population aging and life expectancy increasing, elderly drivers have been increasing quickly in the United States and the heterogeneity among them with age is also increasingly non-ignorable. Based on traffic crash data of Pennsylvania from 2011 to 2019, this study was designed to identify this heterogeneity by quantifying the relationship between age and crash characteristics using linear regression. It is found that for elderly driver-involved crashes, the proportion leading to casualties significantly increases with age. Meanwhile, the proportions at night, on rainy days, on snowy days, and involving driving under the influence (DUI) decrease linearly with age, implying that elderly drivers tend to avoid traveling in risky scenarios. Regarding collision types, elderly driver-involved crashes are mainly composed of angle, rear-end, and hit-fixed-object collisions, proportions of which increase linearly, decrease linearly, and keep consistent with age, respectively.

Abstract Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy.

Abstract Measurements of air–fuel ratio (AFR) and λ (AFRactual/AFRstoich) are crucial for understanding internal combustion engine (ICE) performance. However, current λ sensors suffer from long light-off times (on the order of seconds following a cold start) and limited time resolution. In this study, a four-color mid-infrared laser absorption spectroscopy (LAS) sensor was developed to provide 5 kHz measurements of temperature, CO, CO2, and NO in engine-out exhaust. This LAS sensor was then combined with 1 kHz hydrocarbon (HC) measurements from a flame ionization detector (FID), and the Spindt exhaust gas analysis method to provide 1 kHz measurements of λ. To the authors’ knowledge, this is the first time-resolved measurement of λ during engine cold starts using the full Spindt method. Three tests with various engine AFR calibrations were conducted and analyzed: (1) 10% lean, (2) stoichiometric, and (3) 10% rich.

Abstract In this article, the effects of mixture dilution using EGR or excessive air on adiabatic flame temperature, laminar flame speed, and minimum ignition energy are studied to illustrate the fundamental benefits of lean combustion. An ignition system developing a new active pre-chamber (APC) design was assessed, aimed at improving the indicated thermal efficiency (ITE) of a 1.5 L four-cylinder gasoline direct injection (GDI) engine. The engine combustion process was simulated with the SAGE detailed chemistry model within the CONVERGE CFD tool, assuming the primary reference fuel (PRF) to be a volumetric mixture of 93% iso-octane and 7% n-heptane. The effects of design parameters, such as APC volume, nozzle diameter, and nozzle orientations, on ITE were studied. It was found that the ignition jet velocity from the pre-chamber to the main chamber had a significant impact on the boundary heat losses and combustion phasing.

Abstract In today’s landscape, environmental protection and nature conservation have become paramount across industries, spurring the ever-increasing aspect of decarbonization. Regulatory measures in transportation have shifted focus away from combustion engines, making way for electric mobility, particularly in smaller engines. However, larger applications like ships and stationary power generation face limitations, not enabling an analogous shift to electrification. Instead, the emphasis shifted to zero-carbon fuel alternatives such as hydrogen and ammonia. In addition to minimal carbon-containing emissions due to incineration of lubricating oil, hydrogen combustion with air results in nitrogen oxide emissions, still necessitating quantification for engine operation compliance with legal regulations.

Abstract This study demonstrates the defossilized operation of a heavy-duty port-fuel-injected dual-fuel engine and highlights its potential benefits with minimal retrofitting effort. The investigation focuses on the optical characterization of the in-cylinder processes, ranging from mixture formation, ignition, and combustion, on a fully optically accessible single-cylinder research engine. The article revisits selected operating conditions in a thermodynamic configuration combined with Fourier transform infrared spectroscopy. One approach is to quickly diminish fossil fuel use by retrofitting present engines with decarbonized or defossilized alternatives. As both fuels are oxygenated, a considerable change in the overall ignition limits, air–fuel equivalence ratio, burning rate, and resistance against undesired pre-ignition or knocking is expected, with dire need of characterization.

Abstract A detailed investigation was carried out on the performance, combustion, and emissions of a single-cylinder direct injection hydrogen spark ignition (SI) engine with either a side-mounted direct injection (SDI) or a centrally installed direct injection (CDI) injector. The first part of the study analyzed the performance and emissions characteristics of CDI and SDI engine operations with different injection timings and pressures. This was followed by comparing the engine’s performance and emissions of the CDI and SDI operations at different engine speeds and relative air-to-fuel ratios (lambda) with the optimized injection pressure and timings. Furthermore, the performance and emission attributes of the hydrogen engine with the CDI and SDI setups were conducted at a fixed λ value of 2.75 across a broad spectrum of engine loads. The study’s main outcome demonstrates that both direct injection systems produced near-zero CO2, CO, and HC emissions.

Abstract Fossil fuel reserves are swiftly depleting when consumer demand for these fuels continues to rise. In order to meet the demand and diminish the pollution derived through conventional fuels, it is crucial to employ cleaner fuels made from substitutes such as waste biomass. Also, converting waste biomass to fuel can lower usage of landfills. There are many biomass resources that are suitable for fuel production, out of which groundnut is also a potential feedstock. Groundnut shell biomass was chosen for this study, as it is a waste leftover during shelling of groundnuts for various commercial applications. The procured groundnut shells were converted to oil using pyrolysis process and was distilled. Both the pyrolysis oil and the distilled oil were analyzed using Fourier transform infrared instrument wherein the presence of functional groups such as alcohols, amines, and carboxylic acids were identified.

Abstract Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value.

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 Compared to other age groups, older adults are at more significant risk of hip fracture when they fall. In addition to the higher risk of falls for the elderly, fear of falls can reduce this population’s outdoor activity. Various preventive solutions have been proposed to reduce the risk of hip fractures ranging from wearable hip protectors to indoor flooring systems. A previously developed rubberized asphalt mixture demonstrated the potential to reduce the risk of head injury. In the current study, the capability of the rubberized asphalt sample was evaluated for the risk of hip fracture for an average elderly male and an average elderly female. A previously developed human body model was positioned in a fall configuration that would give the highest impact forces toward regular asphalt.

Abstract After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus® database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.”

Abstract This article investigates the performance of a low-cost throttle-by-wire-system (TbWS) for two-wheeler applications. Mopeds/scooters are still restricted as environmentally harmful. TbWSs can contribute to environmental protection by replacing conventional restrictors. Its consisting of an anisotropic magnetoresistance (AMR) throttle position sensor and a position-controlled stepper motor-driven throttle valve actuator. The decentralized throttle position sensor is operating contactless and acquires redundant data. Throttle valve actuation is realized through a position-controlled stepper motor, sensing its position feedback by Hall effect. Using a PI controller, the stepper motor position is precisely set. Both units transmit and receive data by a CAN bus. Furthermore, fail-safe functions, plausibility checks, calibration algorithms, and energy-saving modes have been implemented.

Abstract A growing interest in electromechanical brakes (EMB) is discernible in the automotive industry finding its climax in an announcement of EMB series production in late 2022 [1]. The introduction of EMB allows for new design opportunities using distributed software on smart actuators. However, additional efforts are needed to ensure continuously high levels of safety even when established design principles in the brake system are changed. This article discusses different safety concepts that could potentially be put in place in EMB actuators. Therefore, safety goals that need to be satisfied by an actuator are derived. Furthermore, three different degrees of complexity are differentiated, evolving to different required electronic control units (ECU) and architectures. Additionally, also the safety of the actuation unit (AU) is considered to realize a holistic safety concept for the actuator. Finally, a conclusion is drawn comparing the different investigated concepts.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract The notable increase in combustion noise in the 7–10 kHz band has become an issue in the development of pre-chamber jet ignition combustion gasoline engines that aim for enhanced thermal efficiency. Combustion noise in such a high-frequency band is often an issue in diesel engine development and is known to be due to resonance in the combustion chamber. However, there are few cases of it becoming a serious issue in gasoline engines, and effective countermeasures have not been established. The authors therefore decided to elucidate the mechanism of high-frequency combustion noise generation specific to this engine, and to investigate effective countermeasures. As the first step, in order to analyze the combustion chamber resonance modes of this engine in detail, calculation analysis using a finite element model and experimental modal analysis using an acoustic excitation speaker were conducted.

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 To address the challenge of directly measuring essential dynamic parameters of vehicles, this article introduces a multi-source information fusion estimation method. Using the intelligent front camera (IFC) sensor to analyze lane line polynomial information and a kinematic model, the vehicle’s lateral velocity and sideslip angle can be determined without extra sensor expenses. After evaluating the strengths and weaknesses of the two aforementioned lateral velocity estimation techniques, a fusion estimation approach for lateral velocity is proposed. This approach extracts the vehicle’s lateral dynamic characteristics to calculate the fusion allocation coefficient. Subsequently, the outcomes from the two lateral velocity estimation techniques are merged, ensuring rapid convergence under steady-state conditions and precise tracking in dynamic scenarios.