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In an engine system, the piston pin is subjected to high loading and severe lubrication conditions, and pin seizures still occur during new engine development. A better understanding of the lubricating oil behavior and the dynamics of the piston pin could lead to cost- effective solutions to mitigate these problems. However, research in this area is still limited due to the complexity of the lubrication and the pin dynamics. In this work, a numerical model that considers structure deformation and oil cavitation was developed to investigate the lubrication and dynamics of the piston pin. The model combines multi-body dynamics and elasto-hydrodynamic lubrication. A routine was established for generating and processing compliance matrices and further optimized to reduce computation time and improve the convergence of the equations. A simple built-in wear model was used to modify the pin bore and small end profiles based on the asperity contact pressures.

Protecting the piston ring and liner interface is critical to the proper operation of internal combustion engines. Specifically, the dry region, which is the portion of the liner above the Top Dead Center (TDC) of the Oil Control Ring (OCR), needs proper lubrication to reduce wear and to maintain sustainability. However, the mechanisms by which oil is distributed to such region have not been investigated. This paper presents the first attempt to understand dry region lubrication by means of the oil-gas interaction below the top ring gap through a combination of experimental and modeling approaches. An optical engine with 2D Laser Induced Fluorescence (2D-LIF) technique was applied to visualize the oil flow below the top ring gap. It was observed that the two vortices downstream the top ring gap can cause oil bridging towards the liner, providing lubrication to the ring-liner interface.

Heavy-duty transportation is one of the sectors that contributes to greenhouse gas emissions. One way to reduce CO2 emissions is to use drop-in fuels. However, when drop-in fuels are used, i.e., higher blends of alternative fuels are added to conventional fuels, solubility problems and precipitation in the fuel can occur. As a result, insolubles in the fuel can clog the fuel filters and interfere with the proper functioning of the injectors. This adversely affects engine performance and increases fuel consumption. These problems are expected to increase with the development of more advanced fuel systems to meet upcoming environmental regulations. This work investigates the composition of the deposits formed inside the injectors of the heavy-duty diesel engine and discusses their formation mechanism. Injectors with internal deposits were collected from field trucks throughout Europe. Similar content, location and structure were found for all the deposits in the studied injectors.

Ground impact caused by road debris can result in very severe fire accident of Electric Vehicles (EV). In order to study the ground impact accidents, a Finite Element model of the battery pack structure is carefully set up according to the practical designs of EVs. Based on this model, the sequence of the deformation process is studied, and the contribution of each component is clarified. Subsequently, four designs, including three enhanced shield plates and one enhanced housing box, are investigated. Results show that the BRAS (Blast Resistant Adaptive Sandwich) shield plate is the most effective structure to decrease the deformation of the battery cells. Compared with the baseline case, which adopts a 6.35-mm-thick aluminum sheet as the shield plate, the BRAS can reduce the shortening of cells by more than 50%. Another type of sandwich structure, the NavTruss, can also improve the safety of battery pack, but not as effectively as the BRAS.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

Internal combustion (IC) engines still power most of the vehicles on road and will likely to remain so in the near future, especially for heavy duty applications in which electrification is typically more challenging. Therefore, continued improvements on IC engines in terms of efficiency and longevity are necessary for a more sustainable transportation sector. Two important design objectives for heavy duty engines with wet liners are to reduce friction loss and to lower the risks of cavitation damages, both of which can be greatly influenced by the piston-liner clearance and the design of the piston skirt. However, engine design optimization is difficult due to the nonlinear interactions between the key design variables and the design objectives, as well as the multi-physics and multi-scale nature of the mechanisms that are relevant to the design objectives.

Typically the combustion in a direct injected compression ignited internal combustion engine is open-loop controlled. The introduction of a cylinder pressure sensor opens up the possibility of a virtual combustion sensor which could enable closed-loop combustion control and thus the potential to counteract effects such as engine part to part variation, component ageing and fuel quality diversity. Closed-loop combustion control requires precise, robust and preferably cheap sensors. This paper presents a virtual cylinder pressure sensor based on the signal from the inexpensive but well proven knock sensor. The method used to convert the knock sensor signal into a pressure estimate included the stages: Phase correcting the raw signal, Filtering the raw signal, Scaling the signal to known thermodynamic laws and provided engine sensors signals and Reconstructing parts of the signal with other known models and assumptions.

Recent studies have shown that for a given RON, fuels with a higher sensitivity (RON-MON) tend to have better antiknock performance at most knock-limited conditions in modern engines. The underlying chemistry behind fuel sensitivity was therefore investigated to understand why this trend occurs. Chemical kinetic models were used to study fuels of varying sensitivities; in particular their autoignition delay times and chemical intermediates were compared. As is well known, non-sensitive fuels tend to be paraffins, while the higher sensitivity fuels tend to be olefins, aromatics, diolefins, napthenes, and alcohols. A more exact relationship between sensitivity and the fuel's chemical structure was not found to be apparent. High sensitivity fuels can have vastly different chemical structures. The results showed that the autoignition delay time (τ) behaved differently at different temperatures. At temperatures below 775 K and above 900 K, τ has a strong temperature dependence.

In an ever-transforming sector such as that of private road transport, major changes in the propulsion systems entail a change in the perception of the noise sources and the annoyance they cause. As compared to the scenario encountered in vehicles equipped with an internal combustion engine (ICE), in electrically propelled vehicles the heating, ventilation, and air conditioning (HVAC) system represents a more prominent source of noise affecting a car’s passenger cabin. By virtue of the quick turnaround, steady state Reynolds-averaged Navier Stokes (RANS)- based noise source models are a handy tool to predict the acoustic power generated by passenger car HVAC blowers. The study shows that the most eminent noise source type is the dipole source associated with fluctuating pressures on solid surfaces.