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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.

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.

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.

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.

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.

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.

MIT Lincoln Laboratory is tasked by the U.S. Federal Aviation Administration to investigate the use of the NEXRAD polarimetric radars* for the remote sensing of icing conditions hazardous to aircraft. A critical aspect of the investigation concerns validation that has relied upon commercial airline icing pilot reports and a dedicated campaign of in situ flights in winter storms. During the month of February in 2012 and 2013, the Convair-580 aircraft operated by the National Research Council of Canada was used for in situ validation of snowstorm characteristics under simultaneous observation by NEXRAD radars in Cleveland, Ohio and Buffalo, New York. The most anisotropic and easily distinguished winter targets to dual pol radar are ice crystals.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

The fuel energy consumption of subsonic air transportation is examined. The focus is on identification and quantification of fundamental engineering design tradeoffs which drive the design of subsonic tube and wing transport aircraft. The sensitivities of energy efficiency to recent and forecast technology developments are also examined.

The objective of this work is to better understand freezing fog/drizzle conditions using observations collected during the Fog Remote Sensing and Modeling project (FRAM-S) that took place at St. John's International Airport, St. John's, NL, Canada. This location was ~1 km away from the Atlantic Ocean coast. During the project, the following measurements at one minute resolution were collected: precipitation rate (PR) and amount, fog/drizzle microphysics, 3D wind speed (Uh) and turbulence (Uh'), visibility (Vis), IR and SW radiative fluxes, temperature (T) and relative humidity (RH), and aerosol observations. The reflectivity and microphysical parameters obtained from the Metek Inc. MRR (Microwave Rain Radar) were also used in the analysis. The measurements were then used to obtain freezing fog/drizzle microphysical characteristics and their relation to visibility.

The dependence of gasoline-electric hybrid vehicle energies on driving conditions and ambient temperature is presented for different drive cycles (2xLA4, 2xLA92, 2xUS06, HWFET and 2xNYCC) and temperatures (20°C and -18°C). The tests were carried out at the Emissions Research and Measurement Division of Environment Canada. Hybrid battery pack current was measured at a frequency of 10 Hz. Regenerative braking energy, charging energy from the engine and battery discharge energy were estimated by using modal speed. The magnitudes of battery energies were found to be directly related to drive cycle properties. Battery discharge energy was very strongly correlated to emission factors of CO₂, while energy recovered by regenerative braking and charging energy from the engine had low to very strong correlations to CO₂ emission factors. CO, NOx and HC had low linear correlations to battery discharge energy.

For this work, a methodology of modeling and predicting fuel consumption in a hybrid vehicle as a function of the engine operating temperature has been developed for cold ambient operation (-7°C, 266°K). This methodology requires two steps: 1) development of a temperature dependent engine brake specific fuel consumption (BSFC) map, and, 2) a data-fitting technique for predicting engine temperature to be used as an input to the temperature dependent BSFC maps. For the first step, response surface methodology (RSM) techniques were applied to generate brake specific fuel consumption (BSFC) maps as a function of the engine thermal state. For the second step, data fitting techniques were also used to fit a simplified lumped capacitance heat transfer model using several experimental datasets. Utilizing these techniques, an analysis of fuel consumption as a function of thermal state across a broad range of engine operating conditions is presented.

Battery packs for Hybrids, Plug-in Hybrids, and Electric Vehicles are assembled from a system of modules (sheets) with a tight sheet metal casing around them. Each module consists of an array of individual cells which vary in the composition of electrodes and separator from one manufacturer to another. In this paper a general procedure is outlined on the development of a constitutive and computational model of a cylindrical cell. Particular emphasis is placed on correct prediction of initiation and propagation of a tearing fracture of the steel can. The computational model correctly predicts rupture of the steel can which could release aggressive chemicals, fumes, or spread the ignited fire to the neighboring cells. The initiation site of skin fracture depends on many factors such as the ductility of the casing material, constitutive behavior of the system of electrodes, and type of loading.

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.

Gas-pressurized space suits are highly resistive to astronaut movement, and this resistance has been previously explained by volume and/or structural effects. This study proposed that an additional effect, pressure effects due to compressing/expanding the internal gas during joint articulation, also inhibits mobility. EMU elbow torque components were quantified through hypobaric testing. Structural effects dominated at low joint angles, and volume effects were found to be the primary torque component at higher angles. Pressure effects were found to be significant only at high joint angles (increased flexion), contributing up to 8.8% of the total torque. These effects are predicted to increase for larger, multi-axis joints. An active regulator system was developed to mitigate pressure effects, and was found to be capable of mitigating repeated pressure spikes caused by volume changes.

The NOx emission and knock characteristics of a PFI engine operating on ethanol/gasoline mixtures were assessed at 1500 and 2000 rpm with λ =1 under Wide-Open-Throttle condition. There was no significant charge cooling due to fuel evaporation. The decrease in NOx emission and exhaust temperature could be explained by the change in adiabatic flame temperature of the mixture. The fuel knock resistance improved significantly with the gasohol so that ignition could be timed at a value much closer or at MBT timing. Changing from 0% to 100% ethanol in the fuel, this combustion phasing improvement led to a 20% increase in NIMEP and 8 percentage points in fuel conversion efficiency at 1500 rpm. At 2000 rpm, where knocking was less severe, the improvement was about half (10% increase in NIMEP and 4 percentage points in fuel conversion efficiency).

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

This paper illustrates a methodology in which complete material-manufacturing process cases for closure panels, reinforcements, and assembly are modeled and compared in order to identify the preferred option for a lightweight closure design. First, process-based cost models are used to predict the cost of lightweighting the closure set of a sample midsized sports utility vehicle (SUV) via material and process substitution. Weight savings are then analyzed using a powertrain simulation to understand the impact of lightweighting on fuel economy. The results are evaluated in the context of production volume and total mass change.

Gasoline-electric hybrid vehicle technology has been gaining widespread acceptance and has the potential to reduce emissions through reduced fuel consumption. In this study, particulate matter number and mass emission rates, organic and elemental carbon compositions, and number-based size distributions were measured from four gasoline-electric hybrid vehicles (2005 Ford Escape Hybrid, 2004 Toyota Prius, 2003 Honda Civic Hybrid, and 2000 Honda Insight). In addition, one small conventional gasoline vehicle (2002 SmartCar) was tested. The vehicles were driven over five driving cycles and at steady-state speeds of 40 and 80 km/h. Each test was performed at 20°C and at -18°C. Testing took place at the Environmental Science & Technology Centre of Environment Canada using conventional chassis dynamometer procedures. Average distance based emission rates are given for each vehicle under each test condition.