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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.

Lubricating oil consumption (LOC) is a direct source of hydrocarbon and particulate emissions from internal combustion engines. LOC also inhibits the lifetime of exhaust aftertreatment system components, preventing their ability to effectively filter out other harmful emissions. Due to its influence on piston ring- bore conformability, bore distortion is arguably the most critical parameter for engine designers to consider in prevention of LOC. Bore distortion also has a significant influence on the contact forces between the piston ring and cylinder wall, which determine the wear rate of the ring and cylinder wall and can cause durability issues. Two drivers of bore distortion: thermal expansion and head bolt stresses, are routinely considered in conformability and contact analyses. Separately, bore distortion/vibration due to piston impact and combustion/cylinder pressures has been previously analyzed in wet liner engines for coolant cavitation and noise considerations.

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.

The increasing demand for environmentally friendly and fuel-efficient transportation and power generation requires further optimization and minimization of friction power losses. With up to 50% of the overall friction, the piston cylinder unit (PCU) shows most potential within the internal combustion engine (ICE) to increase mechanical efficiency. Calculating friction of internal combustion engines, especially the friction contribution from piston rings and skirt, requires detailed knowledge of the dynamics and lubrication regime of the components being in contact. Part I of this research presents a successful match of simulated and measured piston inter-ring pressures at numerous operation points [1] and constitutes the starting point for the comparison of simulated and measured piston group friction forces as presented in this research.

As the piston pin works under significant mechanical load, it is susceptible to wear, seizure, and structural failure, especially in heavy duty internal combustion engines. It has been found that the friction loss associated with the pin is comparable to that of the piston, and can be reduced when the interface geometry is properly modified. However, the mechanism that leads to such friction reduction, as well as the approaches towards further improvement, remain unknown. This work develops a piston pin lubrication model capable of simulating the interaction between the pin, the piston, and the connecting rod. The model integrates dynamics, solid contact, oil transport, and lubrication theory, and applies an efficient numerical scheme with second order accuracy to solve the highly stiff equations. As a first approach, the current model assumes every component to be rigid.

In internal combustion engines, the majority of the friction loss associated with the piston takes place on the thrust side in early expansion stroke. Research has shown that the Friction Mean Effective Pressure (FMEP) of the engine can be reduced if proper modifications to the piston skirt, which is traditionally barrel-shaped, are made. In this research, an existing model was applied for the first time to study the effects of different oil supply strategies for the piston assembly. The model is capable of tracking lubricating oil with the consideration of oil film separation from full film to partial film. It is then used to analyze how the optimized piston skirt profile investigated in a previous study reduces friction.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

Internal combustion engines for plug-in hybrid heavy duty trucks, especially long haul trucks, could play an important role in facilitating use of battery power. Power from a low carbon electricity source could thereby be employed without an unattractive vehicle cost increase or range limitation. The ideal engine should be powered by a widely available affordable liquid fuel, should minimize air pollutant emissions, and should provide lower greenhouse gas emissions. Diesel engines could fall short in meeting these objectives, especially because of high emissions. In this paper we analyze the potential for a flex fuel gasoline-alcohol engine approach for a series hybrid powertrain. In this approach the engine would provide comparable (or possibly greater) efficiency than a diesel engine while also providing 90 around lower NOx emissions than present cleanest diesel engine vehicles. Ethanol or methanol would be employed to increase knock resistance.

The distribution of lubricating oil plays a critical role in determining the friction between piston skirt and cylinder liner, which is one of the major contributors to the total friction loss in internal combustion engines. In this work, based upon the experimental observation an existing model for the piston secondary motion and skirt lubrication was improved with a physics-based model describing the oil film separation from full film to partial film. Then the model was applied to a modern turbo-charged SI engine. The piston-skirt FMEP predicted by the model decreased with larger installation clearance, which was also observed from the measurements using IMEP method at the rated. It was found that the main period of the cycle exhibiting friction reduction is in the expansion stroke when the skirt only contacts the thrust side for all tested installation clearances.

Higher compression ratio and turbocharging, with engine downsizing can enable significant gains in fuel economy but require engine operating conditions that cause engine knock under high load. Engine knock can be avoided by supplying higher-octane fuel under such high load conditions. This study builds on previous MIT papers investigating Octane-On-Demand (OOD) to enable a higher efficiency, higher-boost higher compression-ratio engine. The high-octane fuel for OOD can be obtained through On-Board-Separation (OBS) of alcohol blended gasoline. Fuel from the primary fuel tank filled with commercially available gasoline that contains 10% by volume ethanol (E10) is separated by an organic membrane pervaporation process that produces a 30 to 90% ethanol fuel blend for use when high octane is needed. In addition to previous work, this paper combines modeling of the OBS system with passenger car and medium-duty truck fuel consumption and octane requirements for various driving cycles.

Long-haul and other heavy-duty trucks, presently almost entirely powered by diesel fuel, face challenges meeting worldwide needs for greatly reducing nitrogen oxide (NOx) emissions. Dual-fuel gasoline-alcohol engines could potentially provide a means to cost-effectively meet this need at large scale in the relatively near term. They could also provide reductions in greenhouse gas emissions. These spark ignition (SI) flexible fuel engines can provide operation over a wide fuel range from mainly gasoline use to 100% alcohol use. The alcohol can be ethanol or methanol. Use of stoichiometric operation and a three-way catalytic converter can reduce NOx by around 90% relative to emissions from diesel engines with state of the art exhaust treatment.

In internal combustion engines, a portion of liquid fuel spray may directly land on the liner and mix with oil (lubricant), forming a fuel-oil film (~10μm) that is much thicker than the original oil film (~0.1μm). When the piston retracts in the compression stroke, the fuel-oil mixture may have not been fully vaporized and can be scraped by the top ring into the 1st land crevice and eventually enter the combustion chamber in the format of droplets. Studies have shown that this mechanism is possibly a leading cause for low-speed pre-ignition (LSPI) as the droplets contain oil that has a much lower self-ignition temperature than pure fuel. In this interest, this work aims to study the oil-fuel interactions on the liner during an engine cycle, addressing molecular diffusion (in the liquid film) and vaporization (at the liquid-gas interface) to quantify the amount of fuel and oil that are subject to scraping by the top ring, thereby exploring their implications on LSPI and friction.

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.

To meet future particle mass and particle number standards, gasoline vehicles may require particle control, either by way of an exhaust gas filter and/or engine modifications. Soot levels for gasoline engines are much lower than diesel engines; however, non-combustible material (ash) will be collected that can potentially cause increased backpressure, reduced power, and lower fuel economy. The purpose of this work was to examine the ash loading of gasoline particle filters (GPFs) during rapid aging cycles and at real time low mileages, and compare the filter performances to both fresh and very high mileage filters. Current rapid aging cycles for gasoline exhaust systems are designed to degrade the three-way catalyst washcoat both hydrothermally and chemically to represent full useful life catalysts. The ash generated during rapid aging was low in quantity although similar in quality to real time ash. Filters were also examined after a low mileage break-in of approximately 3000 km.

A new ring pack model has been developed based on the curved beam finite element method. This paper describes the first part of this model: simulating gas pressure in different regions above piston skirt and ring dynamic behavior of two compression rings and a twin-land oil control ring. The model allows separate grid divisions to resolve ring structure dynamics, local force/pressure generation, and gas pressure distribution. Doing so enables the model to capture both global and local processes at their proper length scales. The effects of bore distortion, piston secondary motion, and groove distortion are considered. Gas flows, gas pressure distribution in the ring pack, and ring structural dynamics are coupled with ring-groove and ring-liner interactions, and an implicit scheme is employed to ensure numerical stability. The model is applied to a passenger car engine to demonstrate its ability to predict global and local effects on ring dynamics and oil transport.

Diesel exhaust fluid, DEF, (32.5 wt.% urea aqueous solution) is widely used as the NH3 source for selective catalytic reduction (SCR) of NOx in diesel aftertreatment systems. The transformation of sprayed liquid phase DEF droplets to gas phase NH3 is a complex physical and chemical process. Briefly, it experiences water vaporization, urea thermolysis/decomposition and hydrolysis. Depending on the DEF doser, decomposition reaction tube (DRT) design and operating conditions, incomplete decomposition of injected urea could lead to solid urea deposit formation in the diesel aftertreatment system. The formed deposits could lead to engine back pressure increase and DeNOx performance deterioration etc. The formed urea deposits could be further transformed to chemically more stable substances upon exposure to hot exhaust gas, therefore it is critical to understand this transformation process.

A new ring pack model has been developed based on the curved beam finite element method. This paper describes the second part of this model: simulating oil transport around the ring pack system (two compression rings and one twin-land oil control ring (TLOCR)) through the ring-liner interfaces by solving the oil film thickness on the liner. The ring dynamics model in Part 1 calculates the inter-ring gas pressure and the ring dynamic twist which are used in the ring-liner lubrication model as boundary conditions. Therefore, only in-plane conformability is calculated to obtain the oil film thickness on the liner. Both global process, namely, the structural response of the rings to bore distortion and piston tilt, and local processes, namely, bridging and oil-lube interaction, are considered. The model was applied to a passenger car engine.

This article is concerned with identification of true stress-strain curve under biaxial tension of thermoplastic polymers. A new type of biaxial tension attachment was embedded first in a universal material test machine, which is able to transform unidirectional loading of the test machine to biaxial loading on the specimen with constant velocity. Cruciform specimen geometry was optimized via FE modeling. Three methods of calculating true stress in biaxial tension tests were compared, based on incompressibility assumption, linear elastic theory and inverse engineering method, respectively. The inverse engineering method is more appropriate for thermoplastic polymers since it considers the practical volume change of the material during biaxial tension deformation. The strategy of data processing was established to obtain biaxial tension true stress-strain curves of different thermoplastic polymers.