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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.

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.

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.

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.

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.

While metal fiber filters have successfully shown a high degree of particle retention functionality for various sizes of diesel engines with a low pressure drop and a relatively high filtration efficiency, little is known about the effects of lubricant-derived ash on the fiber filter systems. Sintered metal fiber filters (SMF-DPF), when used downstream from a diesel engine, effectively trap and oxidize diesel particulate matter via an electrically heated regeneration process where a specific voltage and current are applied to the sintered alloy fibers. In this manner the filter media essentially acts as a resistive heater to generate temperatures high enough to oxidize the carbonaceous particulate matter, which is typically in excess of 600°C.

The focus of this study was the characterization and comparison of power-specific exhaust emission rates from a closed-loop small spark-ignited engine fuelled with ethanol and isobutanol gasoline blends. A 4-cycle Kohler ECH-630 engine certified to the Phase 3 emissions standards was operated over the G2 test cycle, a six-mode steady-state test cycle, in its original configuration. This engine was equipped with electronic ignition, electronic fuel injection and an oxygen sensor. Certification gasoline fuel was splash-blended by percent volume with ethanol and isobutanol to result in the test blend levels of E10, E15, iB16 and iB8-E10. Reductions in emission rates of carbon monoxide (up to 12.0% with the ethanol blends and up to 11.4% with the isobutanol blends) were achieved along with a reduction in total hydrocarbons (up to 10.9% with the ethanol blends and up to 8.2% with the isobutanol blends). Nitrogen oxide emissions were decreased by up to 9.8% with the ethanol blends.

This study reported black carbon (BC) mass and solid particle number emissions from a gasoline direct injection (GDI) vehicle and a port fuel injection (PFI) vehicle on splash blended E10 and iB16 fuels over the FTP-75 and US06 drive cycles at standard and cold ambient temperatures. For the FTP-75 drive cycle, the GDI vehicle had lower solid particle number and BC mass emissions from E10 (5.1×1012 particles/mile; 4.2 mg/mile) and iB16 (5.2×1012 particles/mile; 3.9 mg/mile) compared to E0 (7.2×1012 particles/mile; 7.0 mg/mi). Most of the reductions were attributed to the statistically significant reductions during the phases 1 and 2 of the FTP-75 drive cycle. iB16 was also observed to have statistically significant reduction on BC emissions when compared to E0 at cold ambient temperature but E10 did not show such BC reduction. For the PFI vehicle, most of the solid particle number and BC mass emissions were emitted primarily during phase 1 of the FTP-75 drive cycle.

Lubricant viscosity along the engine cylinder liner varies by an order of magnitude due to local temperature variation and vaporization effects. Tremendous potential exists for fuel economy improvement by optimizing local viscosity variations for specific operating conditions. Methods for analytical estimation of friction and wear in the power-cylinder system are reviewed and used to quantify opportunities for improving mechanical efficiency and fuel economy through lubricant formulation tailored specifically to liner temperature distributions. Temperature dependent variations in kinematic viscosity, density, shear thinning, and lubricant composition are investigated. Models incorporating the modified Reynolds equation were used to estimate friction and wear under the top ring and piston skirt of a typical 11.0 liter diesel engine.

Gaseous and particle emissions from a gasoline direct injection (GDI) and a port fuel injection (PFI) vehicle were measured at various ambient temperatures (22°C, -7°C, -18°C). These vehicles were driven over the U.S. Federal Test Procedure 75 (FTP-75) and US06 Supplemental Federal Test Procedure (US06) on Tier 2 certification gasoline (E0) and 10% by volume ethanol (E10). Emissions were analyzed to determine the impact of ambient temperature on exhaust emissions over different driving conditions. Measurements on the GDI vehicle with a gasoline particulate filter (GPF) installed were also made to evaluate the GPF particle filtration efficiency at cold ambient temperatures. The GDI vehicle was found to have better fuel economy than the PFI vehicle at all test conditions. Reduction in ambient temperature increased the fuel consumption for both vehicles, with a much larger impact on the cold-start FTP-75 drive cycle observed than for the hot-start US06 drive cycle.

The formation and transport processes governing the build-up of incombustible ash deposits in diesel particulate filters (DPF) are influenced to a large extent by the filter's operating history. More specifically, the regeneration process, whether active, passive, or some variation of the two, has long been assumed to exert significant influence on the resulting ash characteristics. Until recently, only limited circumstantial evidence was available to describe differences in ash properties and distribution impacting DPF performance for filters subjected to different regeneration strategies. This work presents, for the first time, results from a comprehensive series of evaluations with optically-accessible DPF core samples showing the processes controlling the formation, transport, and interaction of the soot and ash deposits over a range of DPF regeneration conditions.

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.

Current CJ-4 lubricant specifications place chemical limits on diesel engine oil formulations to minimize the accumulation of lubricant-derived ash in diesel particulate filters (DPF). While lubricant additive chemistry plays a strong role in determining the amount and type of ash accumulated in the DPF, a number of additional factors play important roles as well. Relative to soot particles, whose residence time in the DPF is short-lived, ash particles remain in the filter for a significant fraction of the filter's useful life. While it is well-known that the properties (packing density, porosity, permeability) of soot deposits are primarily controlled by the local exhaust conditions at the time of particle deposition in the DPF, the cumulative operating history of the filter plays a much stronger role in controlling the properties and distribution of the accumulated ash.

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.