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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 The development of electric commercial vehicles brought up novel challenges in the design of efficient and reliable air brake systems. The compressor is one of the critical components of the air brake system and is responsible for supplying pressurized air to the brake system. In this study, we aimed to gather essential information regarding the pressure and flow rate requirements for the compressor in the air brake system of electric commercial vehicles. We extensively analyzed the existing air brake systems utilized in conventional commercial vehicles. We examined the performance characteristics of reciprocating compressors traditionally employed in these systems. Recognizing the need for novel compressor designs tailored to electric commercial vehicles, we focused on identifying the specifics such as efficiency, performance characteristics, reliability, and cost of the compressor.

Abstract In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite.

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 The escalating demand for more efficient and sustainable working machines has pushed manufacturers toward adopting electric hybrid technology. Electric powertrains promise significant fuel savings, which are highly dependent on the nature of the duty cycle of the machine. In this study, experimental data measured from a wheel loader in a short-loading Y-cycle is used to exercise a developed mathematical model of a series electric hybrid wheel loader. The efficiency and energy consumption of the studied architecture are analyzed and compared to the consumption of the measured conventional machine that uses a diesel engine and a hydrostatic transmission. The results show at least 30% reduction in fuel consumption by using the proposed series electric hybrid powertrain, the diesel engine rotational speed is steady, and the transient loads are mitigated by the electric powertrain.

Abstract This article presents the design and the analysis of a control logic capable of optimizing vehicle’s energy consumption during a braking maneuver. The idea arose with the purpose of enhancing regeneration and health management in electric vehicles with electro-actuated brakes. Regenerative braking improves energy efficiency and allows a considerable reduction in secondary emissions, but its efficiency is strongly dependent on the state of charge (SoC) of the battery. In the analyzed case, a vehicle equipped with four in-wheel motors (one for each wheel), four electro-actuated brakes, and a battery was considered. The proposed control system can manage and optimize electrical and energy exchanges between the driveline’s components according to the working conditions, monitoring parameters such as SoC of the battery, brake temperature, battery temperature, motor temperature, and acts to optimize the total energy consumption.

Abstract This research looks into how abrasive water jet machining (AWJM) can be used on carbon fiber-reinforced polymer (CFRP) materials, specifically how the kerf characteristics change with respect to change in process parameters. We carefully looked into four important process parameters: stand-off distance (SOD), water pressure (WP), traverse rate (TR), and abrasive mass flow rate (AMFR). The results showed that as SOD goes up, the kerf taper angle goes up because of jet dispersion, but as WP goes up, the angle goes down because jet kinetic energy goes up. The TR was directly related to the kerf taper angle, but it made the process less stable. The kerf drop angle was not greatly changed by AMFR. When it came to kerf top width, SOD made it wider, WP made it narrower, TR made it narrower, and AMFR made it a little wider. When the settings (SOD: 1 mm, WP: 210 MPa, TR: 150 mm/min, AMFR: 200 g/min) were optimized, the kerf taper angle and kerf top width were lowered.

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 This article presents a strategy for the virtual calibration of a large-scale model representing a self-piercing rivet (SPR) connection. The connection is formed between a stack of three AA6016-T4 aluminum sheets and one SPR. The calibration process involves material characterization, a detailed riveting process simulation, virtual joint unit tests, and the final large-scale model calibration. The virtual tests were simulated by detailed solid element FE models of the joint unit. These detailed models were validated using experimental tests, namely peeling, single-lap joint, and cross-tests. The virtual parameter calibration was compared to the experimental calibration and finally applied to component test simulations. The article contains both experiments and numerical models to characterize the mechanical behavior of the SPR connection under large deformation and failure.

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 This study focused on the synthesis and characterization of monodisperse spherical TiO2 nanoparticles doped on the surface with Se (IV) in order to increase the mechanical properties of the bonded joint reinforcing. Work will begin with the synthesis of monodisperse quasi-spherical TiO2 nanoparticles with a modal diameter of less than 20 nm, using the sol-gel technique. Se (IV) selenium surface doping changed the specimen’s chemistry and physics. Different initial concentrations of the doping element will be tested. Next, a physicochemical characterization of the different solid systems will be carried out in order to determine the effect of the doping element on the properties of titanium dioxide. Their morphology and size will be studied through transmission electron microscope observations; volume chemical composition by X-ray diffraction analysis, EDX (energy-dispersive X-ray), and XRF (X-ray fluorescence).

Abstract AISI H13 hot work tool steel is commonly used for applications such as hot forging and hot extrusion in mechanical working operations that face thermal and mechanical stress fluctuations, leading to premature failures. Cryogenic treatment was applied for AISI H13 steel to improve the surface hardness and thereby fatigue resistance. This work involves failure analysis of H13 steel specimens subjected to cryogenic treatment and gas nitriding. The specimens were heated to 1020°C, oil quenched followed by double tempering at 550°C for 2 h, and subsequently, deep cryogenically treated at −185°C in the cryochamber. Gas nitriding was carried out for 24 h at 500°C for 200 μm case depth in NH3 surroundings. The specimens were subjected to rotating bending fatigue at constant amplitude loading at room temperature.

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 Biogas (60% methane–40% CO2 approximately) can be used in the reactivity-controlled compression ignition (RCCI) mode along with a high-reactivity fuel (HRF). In this work dimethyl ether (DME) that can also be produced from renewable sources was used as the HRF as a move toward sustainable power generation. The two-cylinder turbocharged diesel engine modified to work in the DME–biogas RCCI (DMB-RCCI) mode was studied under different proportions of methane (45–95%) in biogas since the quality of this fuel can vary depending on the feedstock and production method. Only a narrow range of biogas to DME ratios could be tolerated in this mode at each output without misfire or knock. Detailed experiments were conducted at brake mean effective pressures (BMEPs) of 3 and 5 bar at a speed of 1500 rpm and comparisons were made with the diesel–biogas dual-fuel and diesel–biogas RCCI modes under similar methane flow rates while the proportion of CO2 was varied.

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 Using ammonia as a carbon-free fuel is a promising way to reduce greenhouse gas emissions in the maritime sector. Due to the challenging fuel properties, like high autoignition temperature, high latent heat of vaporization, and low laminar flame speeds, a dual-fuel combustion process is the most promising way to use ammonia as a fuel in medium-speed engines. Currently, many experimental investigations regarding premixed and diffusive combustion are carried out. A numerical approach has been employed to simulate the complex dual-fuel combustion process to better understand the influences on the diffusive combustion of ammonia ignited by a diesel pilot. The simulation results are validated based on optical investigations conducted in a rapid compression–expansion machine (RCEM). The present work compares a tabulated chemistry simulation approach to complex chemistry-based simulations.

Abstract This article focuses on the development of an active braking control system tailored for electric vehicles. The essence of this system lies in its ability to regulate the slip coefficient to optimize traction during braking, thereby maximizing energy recuperation. In the context of the simulation on enhancing regenerative energy capture in electric vehicles, the use of integral sliding mode control (ISMC) as an alternative for regulating braking performance can be understood through a comparison of two key output variables in braking control systems: wheel deceleration and wheel slip. Traditionally, wheel deceleration has been a controlled variable in braking systems, and it is still utilized in some anti-lock braking systems (ABS). It can be easily measured using a basic wheel encoder. However, the dynamic performance of wheel deceleration control may suffer when there are rapid changes in the road surface.

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Abstract In recent years, the use of cutting fluids has become crucial in hard metal machining. Traditional non-biodegradable cutting fluids have long dominated various industries for machining. This research presents an innovative approach by suggesting a sustainable alternative: a cutting fluid made from a blend of glycerol (GOL) and distilled water (DW). We conducted a thorough investigation, creating 11 different GOL and DW mixtures in 10% weight increments. These mixtures were rigorously tested through 176 experiments with varying loads and rotational speeds. Using Design-Expert software (DES), we identified the optimal composition to be 70% GOL and 30% DW, with the lowest coefficient of friction (CFN). Building on this promising fluid, we explored further improvements by adding three nanoscale additives: Nano-graphite (GHT), zinc oxide (ZnO), and reduced graphene oxide (RGRO) at different weight percentages (0.06%, 0.08%, 0.1%, and 0.3%).