Gas Anxiety and the Charging Choices of Plug-In Hybrid Electric Vehicle Drivers

Plug-in hybrid electric vehicles (PHEV) provide an opportunity to reduce petroleum consumption and greenhouse gas emissions without causing range anxiety. As a result, PHEV drivers are commonly assumed to be less dependent on the availability of charging infrastructure than battery electric vehicle (BEV) drivers. However there is also evidence that PHEVs plug in more often than BEVs because the owners have gas anxiety - a strong desire to avoid using gasoline. This work examines the existence of gas anxiety by analyzing the factors influencing charging decision of PHEV owners. A web-based stated preference survey was conducted and the data was analyzed using a latent class logit model. The result shows that there are two classes of decision making patterns among PHEV owners: those who value gasoline cost and recharging expenditure almost the same (class 1) and those who value gasoline cost more heavily than recharging cost (class 2). Among those in class 2, the amount of money spent on gasoline has much bigger influence on the utility of charging than the amount spent on electricity at the recharging station, which can be interpreted as a form of gas anxiety

Gut mycobiome dysbiosis contributes to the development of hypertension and its response to immunoglobulin light chains

ObjectivesHuman gut microbiome has gained great attention for its proposed roles in the development of hypertension. The fungal microbiome in the human gut (i.e. the mycobiome) is beginning to gain recognition as a fundamental part of our microbiome. However, the existing knowledge of human mycobiome has never revealed the association between gut mycobiome and hypertension. It is known that inflammation and immunity contribute to human hypertension. Here, we sought to investigate whether gut mycobiome could predict the development of hypertension and its association with immunoglobulin light chains.Methods and materialsParticipants were classified into three cohorts: prehypertension (pre-HTN), hypertension (HTN), and normal-tension (NT) based on their blood pressure. Fresh samples were collected, and the ITS transcribed spacer ribosomal RNA gene sequence was performed. An immunoturbidimetric test was used to examine the serum levels of immunological light chains.ResultsSubjects in both of the states of pre-HTN and HTN had different fungal microbiome community compared to the NT group (FDR<0.05). Slightly higher levels of fungal richness and diversity were observed in the groups of pre-HTN and HTN. The relative abundance of Malassezia increased in the HTN group compared to that in the NT group, and the relative abundance of Mortierella enriched in the NT group. For the pre-HTN group, the relative abundance of Malassezia was positively associated with serum the concentration of light chain (LC) κ (r=0.510, P=0.044); for the HTN group, the relative abundance of Mortierella was positively associated with the serum concentration of LC κ (P<0.05), the relative abundance of Malassezia was positively associated with both the serum concentrations of LC κ and LC λ (r>0.30, P<0.05).ConclusionsOur present study demonstrated that gut fungal dysbiosis occurred in the state of prehypertension, and fungal dysbiosis can predict the dysregulation of serum light chains in hypertension patients. Further study on modulating gut fungal community should be focused on balancing the immunological features in hypertension

Discrete Choice Modeling of Plug-in Electric Vehicle Use and Charging Behavior Using Stated Preference Data

Thesis (Ph.D.)--University of Washington, 2019Plug-in Electric Vehicles (PEVs) have the potential of reducing gasoline consumption and greenhouse gas emissions in the transportation sector. The net impacts of PEVs – including upstream emissions from electricity generation and the impact these vehicles place on the electricity grid – depend on both the amount of travel conducted by PEV and locations that those PEVs are charged. This dissertation investigates the vehicle use choices and charging decisions of both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) for both home-based trip tours and long-distance trips using stated preference (SP) data. It presents a novel dynamic discrete choice modeling (DDCM) framework that explicitly accounts for the stochastic nature of the vehicle choice and charging decisions of PEV users: earlier choices on vehicle use and charging influence the utility of the future choices; the expectation of the future options influences those earlier decisions; and choices are made under uncertainty about actual energy consumption and availability of chargers. For home-based trip tours, my results show that BEV users are willing to pay $10-$24 to avoid having to deviate from the originally planned route, which indicates that “range anxiety” of BEV owners – the fear of being stranded in the middle of a trip – is not a crucial issue for home-based trips. Using charging infrastructure development to encourage BEV adoption might be more beneficial than reducing “range anxiety” among the current users, which could entail building charging stations at locations that have more public exposure, such as public parking garages in a city center. When BEVs are on long-distance trips, the cost of deviation is significantly higher: $244, which indicates that BEV owners are likely to be more cautious and view finding a charger off the route much more costly when they are on long-distance trips. Comparing the cost of deviation for home-based tours and long-distance trips, to support the existing users, the most cost-effective places to invest in charging infrastructure are inter-city corridors instead of in-city locations. By comparing the relative size of the coefficient estimates, in this dissertation, I also analyze the monetary value of increasing charging power, moving the charging stations closer to highway exits, and having amenities such as restrooms, restaurants, and Wi-Fi near the charging stations. The comparison between the DDCMs and SDCMs based on simpler decision heuristics shows that for home-based tours, DDCMs only offer a little better prediction rate with a significant cost when it comes to computation time and complexity of model development. For the purpose of demand forecasting of a charging network or site selection for the charging facilities, the SDCMs based on simpler heuristics are recommended for home-based trip tours. For long-distance trips, the charging choices are largely decided by the state of charge (SOC) and deviation, and the characteristics of the charging stations only contribute to a small portion of predictive power. SDCMs outperform the DDCMs for the current sample. However, this could change in the future when the charging network is dense and the characteristics of the charging stations have higher prediction power. For both the home-based tours and long-distance trips, and for both vehicle choices and charging decisions, the decision patterns are likely to be heterogeneous among the PEV owners. The efforts related to the prediction of the future EV charging demand, the policy-making on battery and charging infrastructure development, and the planning/design of the charging network all need to consider these different preferences of the consumers. Due to the heterogeneity of users’ preferences, both increasing battery pack size and reducing station spacing can encourage current BEV owners to use their BEVs for long-distance trips, and one of the two does not substitute the other. Even if a lot of the BEV models offered by the market have 500 miles of range, the density of the public charging network can still play an important role in enabling BEVs for long-distance trips, especially when the battery remains expensive

Car ownership and commuting mode of the “original” residents in a high-density city center: A case study in Shanghai

As a result of rapid urbanization and motorization in China, numerous mega-cities have emerged, and large numbers of people live and work in the city centers. Consequently, developing a public transport-oriented urban structure and promoting sustainable development are major planning strategies for the country. To understand the impact of rail transit on motorization in a high-density city center, we conduct a household travel survey in three neighborhoods around metro stations in the central area of Shanghai. We examine the car buying and commuting behavior of those Shanghai “original” residents who lived there when the city began growing, engulfing them in the center. Studies have shown that 40 percent of commuters in the city center commute outward, following a virtually reversed commute pattern, and the factors significantly affecting their car purchasing choice include their attitude toward cars and transit, household incomes, ownership of the apartments they live in, and the distance between family members’ workplaces and nearest metro stations. Despite easy access to the metro from their home in the city center, those who purchase their apartment units also likely own a car, while those who rent their apartment units are less likely to own a car; however, these odds are still higher than for those who live in an apartment unit inherited from their relatives or provided by their company. In the city center, if a family owns a car, then that car would almost certainly be used for daily commuting. A multinomial logistic model is applied to examine the factors influencing the tendency for using cars. The results show that people’s choices of commuting by alternative modes rather than cars are also shaped by their attitude toward public transportation, but other factors can also subtly change people’s commuting behavior under certain conditions. The commuting distance discourages people from walking and taking buses (but not metro). As the egress distance to the workplace increases, the metro becomes less appealing than cars. Mixed land use encourages people to walk or take buses instead of driving. Older people prefer riding buses and walking to driving, and female respondents tend to prefer walking, cycling, and riding the metro to driving compared to male respondents. These findings contribute to understanding the behavior of people who are familiar with public transportation and how to encourage them to switch from driving cars to alternative transport modes

Effects of a Public Real-Time Multi-Modal Transportation Information Display on Travel Behavior and Attitudes

This study reports on an experiment in downtown Seattle, Washington, to evaluate whether installing a public real-time multi-modal transportation information display screen in an office building lobby caused changes in building occupant self-reported awareness, attitudes, satisfaction, and usage of alternative transportation modes including transit, car-sharing, ride-sourcing, and bike-sharing services. Workers in the test building and two nearby control buildings were surveyed immediately before the screen was installed (N=550) and again six months later (N=455). Little evidence was found that exposure to the real-time display affected respondent travel choices, satisfaction, familiarity, or attitudes toward alternative modes. Although most respondents (70%) had noticed the screen and had generally positive reactions, two-thirds of this group never actually used it. These results, along with building occupant responses to open-ended questions, indicate limited benefits from this installation and suggest that site selection, screen placement, and marketing may help to maximize the effects of these types of displays on traveler satisfaction and mode shifting

Effects of a Public Real-Time Multi-Modal Transportation Information Display on Travel Behavior and Attitudes

This study reports on an experiment in downtown Seattle, Washington, to evaluate whether installing a public real-time multi-modal transportation information display screen in an office building lobby caused changes in building occupant self-reported awareness, attitudes, satisfaction, and usage of alternative transportation modes including transit, car-sharing, ride-sourcing, and bike-sharing services. Workers in the test building and two nearby control buildings were surveyed immediately before the screen was installed (N=550) and again six months later (N=455). Little evidence was found that exposure to the real-time display affected respondent travel choices, satisfaction, familiarity, or attitudes toward alternative modes. Although most respondents (70%) had noticed the screen and had generally positive reactions, two-thirds of this group never actually used it. These results, along with building occupant responses to open-ended questions, indicate limited benefits from this installation and suggest that site selection, screen placement, and marketing may help to maximize the effects of these types of displays on traveler satisfaction and mode shifting

A Two-Stage Wind Grid Inverter with Boost Converter

At present, the conversion efficiency of commercial small wind grid inverter is low, and, in case of low wind speed, the wind energy cannot be used efficiently. In order to resolve this problem, it is necessary to improve the topological structure and control strategy, and design a new small wind grid inverter. In this paper, we apply a two-voltage stage topology with boost converter. The boost circuit is to achieve the maximum power output of the wind energy by the segmented regulation, while the improved inverter topology realizes the overall system function with the former stage circuit. The experimental results show that the new wind grid inverter has superior performance in the low wind speed, and has the high quality energy output. This research has an important practical significance to improve the utilization of renewable energy