High oil prices and nationalist desires to reduce foreign dependency create opportunities for Illinois bituminous coal to be involved in the transportation market. Using Illinois coal for transportation will have varied environmental effects depending on the method of involvement. To determine these effects, this study calculated CO2 emission for gasoline and eight other vehicle propulsion methods involving Illinois coal for 100,000 miles traveled. The vehicle propulsion methods considered were electricity from Pulverized coal in a Sub-Critical power cycle (PSC), electricity from Integrated Gasification Combined power Cycle (IGCC), electricity from an Ultra Super Critical power cycle (USC), ethanol, butanol, Fischer-Tropsch (FT) diesel, hydrogen, and a combination IGCC/ethanol system to propel vehicles that use their respective fuels. Results show USC, IGCC, PSC, and hydrogen emitted the lowest CO2 with a net of 69,494, 72,866, 75,752, and 81,587 lb CO2/100,000 miles respectively. The base-line gasoline method emitted 99,170 lb CO2, while ethanol, butanol, and IGCC/ethanol methods emitted 97,078, 106,338, and 92,449lb CO2, respectively. The highest CO2 emission came from Fischer-Tropsch diesel with 180,560 lb CO2. It was concluded that life cycle energy efficiency and CO2 offset were the most influential factors for CO2 emissions per 100,000 miles.

Road transport is responsible for a significant share of the global GHG emissions. In order to address the increasing trend of road vehicle emissions, due to its heavy reliance on oil, Nordic countries have set ambitious goals and policies for the reduction of road transport GHG emissions. Despite the fact that the latest developments in the passenger car segment are leading towards the progressive electrification of the fleet, the decarbonization of heavy-duty vehicle segment presents significant challenges that are yet to be overcome. This study focuses, on the first part, on the regulatory framework of fuel economy standards of road vehicles, highlighting the absence of a European regulation on fuel efficiency for the heavy-duty sector. Energy efficiency technologies can be grouped mainly in vehicle technologies, driveline and powertrain technologies, and alternative fuels. The fuel efficiency of HDVs can be positively improved at different vehicle levels, but the technology benefit and its economic feasibility are heavily dependent on the vehicle type and the operational cycle considered. The electrification pathway has the potential of reducing the carbon emission to a great extent, but the current battery technologies have proven to be not cost efficient for the heavy vehicles, because of the high purchase price and the low range, related to the battery cost and inferior energy density compared to conventional liquid fuels.   A scenario development model has been created in order to estimate and quantify the impact of future developments and emission reduction measures in Finland, Sweden and Norway for the timeframe 2016-2050, with a focus on 2030 results. Two scenarios concerning the powertrain developments of heavy-duty vehicles and buses have been created, a conservative scenario and electric scenario, as well as vehicle efficiency improvements and fuel consumption scenarios. Additional sets of parameters have been estimated as input for the model, such as national transport need and load assumptions. The results highlight the challenges of achieving the national GHG emission reduction targets with the current measures in all three countries. The slow fleet renewal rates and the high forecasted increase of transport need limit the benefits of alternative and more efficient powertrains introduced in the fleet by new vehicles. The heavy-duty transport is expected to maintain its heavy reliance on diesel fuel and hinder the improvements of the light-duty segments. A holistic approach is needed to reduce the GHG emissions from road transport, including more efficient powertrains, higher biofuel shares and progressive electrification.

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With electric vehicles becoming increasingly prevalent in the automotive market consumers are becoming more conscientious of total driving range. In light of this trend, reliable and accurate modeling methods are necessary to aid the development of more energy efficient vehicles with greater drivable range. Many methods exist for evaluating energy consumption of current and future vehicle designs over the US certification drive cycles. This work focuses on utilizing the well-established Willans line approximation and proposes a simplified modeling method to determine electric vehicle energy consumption and powertrain efficiency. First, a backwards physics-based model is applied to determine tractive effort at the wheel to meet US certification drive cycle demand. Second, the Willans line approximation then augments the tractive effort model and parameterizes the vehicle powertrain to establish a bi-directional power flow method. This bi-directional approach separates propel and brake phases of the vehicle over the certification City and Highway drive cycles to successfully isolate the vehicle powertrain from non-intrinsic losses, such as parasitic accessory loads. The proposed method of bi-directional modeling and parameter tuning provides significant insight to the efficiency, losses, and energy consumption of a modeled electric vehicle strictly using publicly available test data. Results are presented for eight electric vehicles with production years varying from 2016 to 2021. These electric vehicles are chosen to encapsulate the electric vehicle market as performance electric vehicles to smaller commuter electric vehicles are selected. All vehicles are modeled with an accessory load constrained between 300 and 850 W and a regenerative braking ("regen") low-speed cutoff of 5 mph with six of the eight vehicles modeled with a regenerative braking fraction of 94%. The bi-directional Willans line is then tuned to reach agreement with the net EPA energy consumption test data for each vehicle with the results presented as representative of the chosen vehicle. Lastly, a transfer function relating major model inputs to the output is derived and lends considerable insight for the sensitivity of the modeling method. Sensitivity of the proposed modeling method is conducted for a 2017 BMW i3 with the model deemed reasonably resilient to changes in input parameters. The model is most sensitive to changes in powertrain marginal efficiency with a 6% decrease of marginal efficiency leading to a 0.404 kW and 0.793 kW cycle average net battery power increase for the City and Highway drive cycles respectively. Additionally, the model is also sensitive to changes in vehicle accessory load with a direct relationship between increases of vehicle accessory load to increases of cycle average net battery power for the City and Highway cycles. The sensitivity results justify the use of the proposed model as a method for evaluating vehicle energy consumption and powertrain efficiency solely using publicly available test data. / Master of Science / Developing robust and accurate methods for analyzing electric vehicle energy consumption and powertrain efficiency is of great interest. For the purposes of this paper, powertrain refers to a motor / inverter pair which is coupled to a simple gear reduction for torque multiplication. Many vehicles are designed with motors of varying power and torque capabilities which can present challenges when attempting to effectively compare electric vehicles from different manufacturers. The proposed modeling method presented in this work utilizes public test data to derive detailed vehicle and powertrain information. Vehicle energy consumption is also modeled and compared to net EPA test data. Eight electric vehicles are modeled with each vehicle representing a specific segment of the current electric vehicle market. A bi-directional Willans line is applied to model the propel and brake phases of each electric vehicle over the US certification drive cycles. The bi-directional approach effectively isolates the vehicle powertrain from non-intrinsic losses. From the derived powertrain parameters and modeled energy consumption, the proposed method is deemed accurate and highly useful for translating public test data to detailed vehicle information. Lastly, a sensitivity analysis is presented with the proposed method deemed reasonably resilient to changes in input parameters. The modeling method is most sensitive to changes of powertrain marginal efficiency and vehicle accessory load but constraining these inputs to reasonable ranges for electric vehicles proves sufficient.

<p> Integrating solar energy into the electric vehicle (EV) market alleviates the demand for</p> <p>fossil fuels used to generate the electricity used to power these vehicles. Integrated solar panels</p> <p>provide a new method of power generation for an electric vehicle, but researchers must consider</p> <p>new dependent variables such as drag in the figure of vehicle efficiency. For the solar array to be</p> <p>deemed a viable option for power generation, the solar array must generate enough energy to</p> <p>overcome the added weight and aerodynamic drag forces the solar system introduces. The thesis</p> <p>explores the application of photovoltaic modules for power generation in an EV system.</p> <p>Researchers installed an off-the-shelf solar module on the roof of an EV and investigated the</p> <p>system to explore the efficiency tradeoffs. The research sought to identify an optimized solar</p> <p>panel configuration for minimized drag based on maximized panel surface irradiance, cooling,</p> <p>and array output voltage parameters. The study utilized computational fluid dynamics modeling,</p> <p>wind tunnel testing, and full-scale track testing to analyze the system. The results of this study</p> <p>provide an optimized configuration for a Renogy RNG-100D atop a Chevrolet Bolt. The system</p> <p>was considered optimal at a tilt angle of zero degrees when in motion. The performance benefits</p> <p>due to the increased angle of the solar panel tilt were deemed insufficient in overcoming the</p> <p>aerodynamic drag forces introduced into the system while in motion.</p>