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Abstract Lithium ion batteries offer an attractive solution for powering electric vehicles due to their relatively high specific energy and specific power, however, the temperature of the batteries greatly affects their performance as well as cycle life. In this work, an empirical equation characterizing the battery's electrical behavior is coupled with a lumped thermal model to analyze the electrical and thermal behavior of the 18650 Lithium Iron Phosphate cell. Under constant current discharging mode, the cell temperature increases with increasing charge/discharge rates. The dynamic behavior of the battery is also analyzed under a Simplified Federal Urban Driving Schedule and it is found that heat generated from the battery during this cycle is negligible. Simulation results are validated with experimental data. The validated single cell model is then extended to study the dynamic behavior of an electric vehicle battery pack. The modeling results predict that more heat is generated on an aggressive US06 driving cycle as compared to UDDS and HWFET cycle. An extensive thermal management system is needed for the electric vehicle battery pack especially during aggressive driving conditions to ensure that the cells are maintained within the desirable operating limits and temperature uniformity is achieved between the cells.

Abstract Lithium ion batteries offer an attractive solution for powering electric vehicles due to their relatively high specific energy and specific power, however, the temperature of the batteries greatly affects their performance as well as cycle life. In this work, an empirical equation characterizing the battery's electrical behavior is coupled with a lumped thermal model to analyze the electrical and thermal behavior of the 18650 Lithium Iron Phosphate cell. Under constant current discharging mode, the cell temperature increases with increasing charge/discharge rates. The dynamic behavior of the battery is also analyzed under a Simplified Federal Urban Driving Schedule and it is found that heat generated from the battery during this cycle is negligible. Simulation results are validated with experimental data. The validated single cell model is then extended to study the dynamic behavior of an electric vehicle battery pack. The modeling results predict that more heat is generated on an aggressive US06 driving cycle as compared to UDDS and HWFET cycle. An extensive thermal management system is needed for the electric vehicle battery pack especially during aggressive driving conditions to ensure that the cells are maintained within the desirable operating limits and temperature uniformity is achieved between the cells.

Indicators of sustainability and environmental performance can be useful for comparing modes, discerning trends, and formulating appropriate policies. This paper considers the performance of U.S. transportation service sectors through use of 1992 and 1997 benchmark input–output models. Use of these models permits assessment of not only the direct performance of the sectors but also the supply chain impacts required for operation of the transportation sectors. Consideration of indirect impacts is critical for assessment of the overall costs and impacts of particular products or services. Six transportation service sectors (air, rail, water, truck, transit, and pipeline) are examined. Economic impact, energy, greenhouse gas emissions, and toxic emissions are examined. The transportation sectors use large amounts of energy, both in total and per dollar of output and on a per service basis. Pipeline and water transportation have particularly large energy requirements per dollar of output, likely reflecting higher energy intensity and lower labor intensity in these modes. Truck transportation is the most energy intensive of the freight transportation modes per ton-mile of service, but it has a trend toward greater energy efficiency. For greenhouse gas emissions, truck, water, and air transportation have the highest emissions per dollar of output. Water transportation freight rates are sufficiently low that emissions on a per ton-mile basis would be correspondingly low. Finally, the supply chain (indirect) toxic emissions per dollar of output are highest for rail and pipeline transportation. There is considerable work to be done to improve the overall sustainability of the different transportation modes.

Indicators of sustainability and environmental performance can be useful for comparing modes, discerning trends, and formulating appropriate policies. This paper considers the performance of U.S. transportation service sectors through use of 1992 and 1997 benchmark input–output models. Use of these models permits assessment of not only the direct performance of the sectors but also the supply chain impacts required for operation of the transportation sectors. Consideration of indirect impacts is critical for assessment of the overall costs and impacts of particular products or services. Six transportation service sectors (air, rail, water, truck, transit, and pipeline) are examined. Economic impact, energy, greenhouse gas emissions, and toxic emissions are examined. The transportation sectors use large amounts of energy, both in total and per dollar of output and on a per service basis. Pipeline and water transportation have particularly large energy requirements per dollar of output, likely reflecting higher energy intensity and lower labor intensity in these modes. Truck transportation is the most energy intensive of the freight transportation modes per ton-mile of service, but it has a trend toward greater energy efficiency. For greenhouse gas emissions, truck, water, and air transportation have the highest emissions per dollar of output. Water transportation freight rates are sufficiently low that emissions on a per ton-mile basis would be correspondingly low. Finally, the supply chain (indirect) toxic emissions per dollar of output are highest for rail and pipeline transportation. There is considerable work to be done to improve the overall sustainability of the different transportation modes.

In this study, a mixed integer linear programming model that integrates multimodal transport—truck and rail—into the switchgrass-based bioethanol supply chain was formulated. The objective of this study was to minimize the total cost for cultivation and harvesting, infrastructure, the storage process, bioethanol production, and transportation. Strategic decisions, including the number and location of intermodal facilities and biorefineries, and tactical decisions, such as the amount of biomass shipped, processed, and converted into bioethanol, were validated by using North Dakota as a case study. It was found that the multimodal transport scenario was more cost effective than a single mode of transport (truck) and resulted in a lower cost for bioethanol. A sensitivity analysis was conducted to demonstrate the impact of key factors in the decision to use multimodal transport in a switchgrass-based bioethanol supply chain and on the cost of bioethanol.

In this study, a mixed integer linear programming model that integrates multimodal transport—truck and rail—into the switchgrass-based bioethanol supply chain was formulated. The objective of this study was to minimize the total cost for cultivation and harvesting, infrastructure, the storage process, bioethanol production, and transportation. Strategic decisions, including the number and location of intermodal facilities and biorefineries, and tactical decisions, such as the amount of biomass shipped, processed, and converted into bioethanol, were validated by using North Dakota as a case study. It was found that the multimodal transport scenario was more cost effective than a single mode of transport (truck) and resulted in a lower cost for bioethanol. A sensitivity analysis was conducted to demonstrate the impact of key factors in the decision to use multimodal transport in a switchgrass-based bioethanol supply chain and on the cost of bioethanol.

The era of the electrified transportation system is fast approaching. Although the socioeconomic and environmental benefits of electric vehicles (EVs) have contributed to their large-scale utilization, it has also created a huge load demand on the existing power grids throughout the world. Moreover, fast, super-fast, and ultra-super-fast charging stations are under development, some of which are now in the markets. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. Moreover, EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper addresses these issues and provides a review of the state-of-the-art EV technologies and their impacts on power grids. This paper also investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. A practical electrical grid of Wollongong, New South Wales, Australia has been considered in this work to separately analyze the impacts of constant current (CC) and constant voltage (CV) charging modes upon the grid. Furthermore, design and modeling of three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and ChargePoint Express 200), with separate CC-CV charging modes of the DC fast chargers have been incorporated. To quantify the impacts, two separate scenarios were examined using a simulation platform, with case studies conducted to determine the impacts on the power grid. The first scenario involved three fast charging stations, while the second scenario featured ten stations that were able to charge six and twenty electric vehicles respectively, with various load combinations considered. Each of these scenarios was analyzed under different conditions to ...