Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Behavioral Realism Of Plug In Electric Vehicle Usage
Download Behavioral Realism Of Plug In Electric Vehicle Usage full books in PDF, epub, and Kindle. Read online Behavioral Realism Of Plug In Electric Vehicle Usage ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Behavioral Realism of Plug-in Electric Vehicle Usage by : Seshadri Srinivasa Raghavan
Download or read book Behavioral Realism of Plug-in Electric Vehicle Usage written by Seshadri Srinivasa Raghavan and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerating the adoption of plug-in electric vehicles (PEVs), is critical to reduce GHG emissions in the light duty vehicle sector. Conventional PEV usage and GHG assessments are largely based on assumptions drawn from stated preferences and choice experiments of potential or current PEV owners, or self-reported travel and refueling diaries of mainstream internal combustion engine(ICE) users. This dissertation focuses on observed behavior of current PEV users. I present three studies that seek to improve our understanding of PEV driving and charging typified by two levels of disaggregation- vehicle level and household level. First study develops an analytical procedure to quantify what aspects of driving and charging behavior contributes to the gap between observed PHEV Utility Factors and Society of Automotive Engineers (SAE) J2841 expectations. Results indicated that depending on the PHEV range, roughly ±45% of deviations is attributable charging behavior. Daily mileage was responsible for -20% to +3% of deviation. Annual mileage and effective charge depleting range achieved on-road influenced the UF deviation by ±25% and -20% to -4% respectively. In the second study, driving and charging behavior differences between short-range (20 miles or less) and long-range (35 miles or more) PHEVs are investigated. It was found that diversity of charging locations is positively associated with electric miles from short-range PHEVs whereas encouraging more home charging increases the electrification benefits of longer-range PHEVs. Third study quantifies the well-to-wheel GHG mitigation potential of Nissan Leaf, Chevrolet Bolt and Tesla Model S at the household level using a multi-year actual usage data from 73 two-car (single BEV and single ICE) California households. Analysis shows that on average 25% of Leaf and Bolt, and 30% of Tesla household's GHG can be reduced from their current levels by driving the BEV instead of the ICE. Upgrading to a longer-range efficiency oriented BEV and fully charging overnight can mitigate an additional 10-15% household GHG. Upgrading to longer-range sportier performance oriented BEV nearly offset the GHG abatement benefits, but it electrifies the highest share of household miles.
Book Synopsis Discrete Choice Modeling of Plug-in Electric Vehicle Use and Charging Behavior Using Stated Preference Data by : Yanbo Ge
Download or read book Discrete Choice Modeling of Plug-in Electric Vehicle Use and Charging Behavior Using Stated Preference Data written by Yanbo Ge and published by . This book was released on 2019 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plug-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.
Book Synopsis Advances in Behavioral Economics by : Colin F. Camerer
Download or read book Advances in Behavioral Economics written by Colin F. Camerer and published by Princeton University Press. This book was released on 2011-12-12 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: Twenty years ago, behavioral economics did not exist as a field. Most economists were deeply skeptical--even antagonistic--toward the idea of importing insights from psychology into their field. Today, behavioral economics has become virtually mainstream. It is well represented in prominent journals and top economics departments, and behavioral economists, including several contributors to this volume, have garnered some of the most prestigious awards in the profession. This book assembles the most important papers on behavioral economics published since around 1990. Among the 25 articles are many that update and extend earlier foundational contributions, as well as cutting-edge papers that break new theoretical and empirical ground. Advances in Behavioral Economics will serve as the definitive one-volume resource for those who want to familiarize themselves with the new field or keep up-to-date with the latest developments. It will not only be a core text for students, but will be consulted widely by professional economists, as well as psychologists and social scientists with an interest in how behavioral insights are being applied in economics. The articles, which follow Colin Camerer and George Loewenstein's introduction, are by the editors, George A. Akerlof, Linda Babcock, Shlomo Benartzi, Vincent P. Crawford, Peter Diamond, Ernst Fehr, Robert H. Frank, Shane Frederick, Simon Gächter, David Genesove, Itzhak Gilboa, Uri Gneezy, Robert M. Hutchens, Daniel Kahneman, Jack L. Knetsch, David Laibson, Christopher Mayer, Terrance Odean, Ted O'Donoghue, Aldo Rustichini, David Schmeidler, Klaus M. Schmidt, Eldar Shafir, Hersh M. Shefrin, Chris Starmer, Richard H. Thaler, Amos Tversky, and Janet L. Yellen.
Book Synopsis Data-driven Behavior Analysis and Implications in Plug-in Electric Vehicle Policy Studies by : Wei Ji
Download or read book Data-driven Behavior Analysis and Implications in Plug-in Electric Vehicle Policy Studies written by Wei Ji and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The adoption of plug-in electric vehicles (PEVs) is considered to be a potential solution to reduce transportation-related emissions. People’s vehicle choice and driving behavior will have important implications for the realized emissions reductions from PEVs. Therefore, PEV-related policy studies require good understanding of human behavior. Traditional approaches to analyze travel behavior are mostly to build analytic models based on assumptions because of the limited accuracy and information of data. With the development of sensor technology, there are more methods than ever to collect accurate and informative behavioral data, so the crucial consideration is how to creatively use these data to better understand people’s behavior. This dissertation proposed some data-driven approaches to simulate behavior and provided a discussion of the implications for three PEV-related topics. The first study explored the potential of greenhouse gas (GHG) reductions that can be achieved with adoption of PEVs in California by simulating vehicles’ emissions based on tracing data. It was found that assigning the right model of PEVs to drivers can help to reduce annual GHG emissions by 65%, compared to everyone driving a Toyota Corolla. The second study presented a tool to evaluate the spatial distribution of fast charging demand and to assess how much a charger in a certain location would be used based on travel diary. Scenario analysis illustrated that en-route fast charging demand will shift from primarily inside metro areas to long distance corridors outside metro areas as the battery size increases. The third study estimated the value of Clean Air Vehicle (CAV) decals by simulating the frequency of PEV owners’ access to high occupancy vehicle/toll (HOV/T) lanes based on survey data. The results indicated that the CAV Decals Program is one of the most attractive incentive policies, but there is spatial heterogeneity of CAV decal value across different regions.
Book Synopsis Who’s Driving Electric Cars by : Marcello Contestabile
Download or read book Who’s Driving Electric Cars written by Marcello Contestabile and published by Springer Nature. This book was released on 2020-03-17 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive yet accessible snapshot of the latest consumer research on the adoption and use of electric vehicles. It discusses the importance of developing a better understanding of consumer behavior in relation to electric vehicles, and the advantages that can be gained from the growing number of electric vehicle users, who can now be studied directly. In turn, it systematically analyzes the leading markets for electric vehicles in North America, Europe and Asia. Bringing together the experience and expertise of authoritative researchers and practicing professionals, the book shares a wide range of empirical data obtained at the national level and summarizes the general lessons learned. The last part of the book discusses policy-relevant insights, forecasts the future evolution of the field in terms of methods and data availability, and addresses several key questions that policymakers and other stakeholders are currently facing.
Book Synopsis Charging Behavior, Driving Patterns, and EVMT for PHEV 20s Over Time by : Laura Christine Cackette
Download or read book Charging Behavior, Driving Patterns, and EVMT for PHEV 20s Over Time written by Laura Christine Cackette and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of real world data from plug-in hybrid electric vehicles (PHEVs) in California was conducted with the objective of characterizing how owners charge and drive PHEVs with 20 miles of battery capacity (PHEV 20s). A large volume of in-use data, collected by a major vehicle manufacturer over a period of almost three years, was examined. Results and discussion focus on several key points of interest: how often PHEV 20 owners plug in and how that changes over time; how other variables such as fuel price may affect plug-in frequency; and how many electric miles are driven as a result of charging and travel behavior. On average, owners plug their vehicles in more than once per active day; although, some owners stop doing so and cause rates of not charging to increase over time. They also drive around 52 miles per active day, with 17 of those powered by electricity. Therefore, PHEV 20s in California achieve more than 30% eVMT (electric vehicle miles traveled) on average.
Book Synopsis The Global Rise of the Modern Plug-In Electric Vehicle by : John D. Graham
Download or read book The Global Rise of the Modern Plug-In Electric Vehicle written by John D. Graham and published by Edward Elgar Publishing. This book was released on 2021-04-30 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: We may be standing on the precipice of a revolution in propulsion not seen since the internal combustion engine replaced the horse and buggy. The anticipated proliferation of electric cars will influence the daily lives of motorists, the economies of different countries and regions, urban air quality and global climate change. If you want to understand how quickly the transition is likely to occur, and the factors that will influence the predictions of the pace of the transition, this book will be an illuminating read.
Book Synopsis Analyzing the Impact of Plug-in Electric Vehicle's Charging Load on the Grid Based on Driver's Personal Attitudes Towards Pev Usage and Charging by : Mehran Mustafa
Download or read book Analyzing the Impact of Plug-in Electric Vehicle's Charging Load on the Grid Based on Driver's Personal Attitudes Towards Pev Usage and Charging written by Mehran Mustafa and published by . This book was released on 2021 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, the transport sector is responsible for nearly one-quarter of global energy-related direct carbon-dioxide (CO2) emissions and is a significant contributor to air pollution. In the United States, the transportation sector has the highest share (28%) in the mix of green-house gas (GHG) sources. Some of the more developed nations across the globe are now committed to improve the climate and air quality. Countries like China, Europe and the United States are front runners in introducing ambitions policies to incentivize the production and adoption of plug-in electric vehicles (PEV's). Along with the expected benefits of PEV uptake, large scale deployment poses a challenge for the electric grid, especially at the distribution level, since the charging load of an PEV is substantial. This load is dependent not only on the characteristics of the PEV, but also on its use and charging habits of its user(s). Since a PEV can be directly plugged into the grid at any available point, which may be spatially anywhere in the utility's service area, it is important to model its accurate use and charging behavior of the users. Having precise knowledge of the load profile, the utilities can have a better economic solution to balancing the supply and demand. In this dissertation, an agent-based model is developed that estimates the impact of charging load of PEVs on the grid. It is based on reasonably realistic diverse human behavior pertaining to day-to-day driving patterns and charging practices and their effect on each other. The model portrays the heterogenous, spatial and temporal nature of this load, which depends on the habits and the interaction among different agents. The model mimics the heterogeneity of choices made by human drivers and its effect on the charging choices of other drivers, which is an important element to consider when depicting human behavior. The model uses travel statistics of conventional personally owned vehicles (POVs) from the National Household Travel Survey (NHTS) conducted by the Federal Highway Administration (FHWA) across different states of the United States from 2016-2017. The travel needs are modified to incorporate the effect of EV's limited range and charging time requirements. A modified GIS map of Collinsville, IL, is used to implement the spatial requirements of travel, with, which highlight exact load points. The agent's travel and charging choices are modelled with heterogenous rules of engagement with the environment and other agents. Common psychological effects of limited range, long charging times, and range anticipation are applied heterogeneously to all agents to create a macro environment. The resulting charging load is superimposed on existing substation transformer load and voltage profile is analyzed to study the impact of different charging strategies and charging infrastructure availability. Different case studies are analyzed to investigate the effect of the aggregated load of multiple charging points in the respective service areas of the distribution transformers.
Book Synopsis Households' Plug-in Hybrid Electric Vehicle Recharging Behavior by : Jamie Davies
Download or read book Households' Plug-in Hybrid Electric Vehicle Recharging Behavior written by Jamie Davies and published by . This book was released on 2010 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Improving the Behavioral Realism of Global Integrated Assessment Models by :
Download or read book Improving the Behavioral Realism of Global Integrated Assessment Models written by and published by . This book was released on 2016 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and behaviorally-realistic model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. Moreover, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.
Book Synopsis Impact of Observed Travel and Recharging Behavior, Simulated Workplace Charging Infrastructure, and Vehicle Design on PHEV Utility Factors (UF), Total Charge Depleting (CD) Driving and Time of Day (TOD) Grid Demand by : Jamie Davies-Shawhyde
Download or read book Impact of Observed Travel and Recharging Behavior, Simulated Workplace Charging Infrastructure, and Vehicle Design on PHEV Utility Factors (UF), Total Charge Depleting (CD) Driving and Time of Day (TOD) Grid Demand written by Jamie Davies-Shawhyde and published by . This book was released on 2011 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Transitions to Alternative Vehicles and Fuels by : National Research Council
Download or read book Transitions to Alternative Vehicles and Fuels written by National Research Council and published by National Academies Press. This book was released on 2013-04-14 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.
Book Synopsis The Multi-Agent Transport Simulation MATSim by : Andreas Horni
Download or read book The Multi-Agent Transport Simulation MATSim written by Andreas Horni and published by Ubiquity Press. This book was released on 2016-08-10 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
Book Synopsis Topics in Sustainable Transportation by : Mobashwir Khan
Download or read book Topics in Sustainable Transportation written by Mobashwir Khan and published by . This book was released on 2012 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first part of this thesis, GPS data for a year's worth of travel by 255 Seattle households is used to illuminate how plug-in electric vehicles (PEVs) can match household needs. Data from all vehicles in each of these households were analyzed at a disaggregate level primarily to determine whether each household would be able to adopt various types of PEVs without significant issues in meeting travel needs. The results suggest that a battery-electric vehicle (BEV) with 100 miles of all-electric range (AER) should meet the needs of 50% of Seattle's one-vehicle households and the needs of 80% of the multiple-vehicle households, when households charge just once a day and rely on another vehicle or mode just 4 days a year. Moreover, the average one-vehicle Seattle household uses each vehicle 23 miles per day and should be able to electrify close to 80% of its miles, while meeting all its travel needs, using a plug-in hybrid electric vehicle with 40-mile all-electric-range (PHEV40). Households owning two or more vehicles can electrify 50 to 70% of their total household miles using a PHEV40, depending on how they assign the vehicle across drivers each day. Cost comparisons between the average single-vehicle household owning a Chevrolet Cruze versus a Volt PHEV suggest that, when gas prices are $3.50 per gallon and electricity rates are 11.2 ct per kWh, the Volt will save the household $535 per year in energy/fuel costs. Similarly, the Toyota Prius PHEV will provide an annual savings of $538 per year over the Corolla. The results developed in this research provide valuable insights into the role of AER on PEV adoption feasibility and operating cost differences. The second part of this thesis uses detailed travel data from the Seattle metropolitan area to evaluate the effects of built-environment variables on the use of non-motorized (bike + walk) modes of transport. Several model specifications are used to understand and explain non-motorized travel behavior in terms of household, person and built-environment variables. Land-use measures like land-use mix, density, and accessibility indices were also created and incorporated as covariates to appreciate their marginal effects. The models include a count model for household vehicle ownership levels, a binary choice model for the decision to stay within versus departing one's origin zone (i.e., intra- versus inter-zonal trip-making), discrete choice models for destination choices and mode choices, and a zero-inflated negative binomial model for non-motorized trip counts per household. The mode and destination choice models were estimated separately for interzonal and intrazonal trips and for each of three different trip types (home-based work, home-based non-work, and non-home-based), to recognize the distinct behaviors at play when making shorter versus longer trips and different types of trips. This comprehensive set of models highlights how built-environment variables -- like the number and type of intersections present around one's origin and destination, the number of bus stops available within a certain radius, household and jobs densities, parking prices, land use mixing, and walk-based accessibility -- can significantly shape the pattern of one's non-motorized movement. The results underscore the importance of street connectivity (quantified as the number of 3-way and 4-way intersections in a half-mile radius), higher bus stop density, and greater non-motorized access in promoting lower vehicle ownership levels (after controlling for household size, income, neighborhood density and so forth), higher rates of non-motorized trip generation (per day), and higher likelihoods of non-motorized mode choices. Destination choices are also important for mode choices, and local trips lend themselves to more non-motorized options than more distance trips. Intrazonal trip likelihoods rose with higher street connectivity, transit availability, and land use mixing. For example, the results suggest that an increase in the land-use mix index by 10% would increase the probability of choosing to travel within the zone by 12%. As expected destinations with greater population and job numbers (attraction), located closer (to a trip's origin), offering lower parking prices and greater transit availability, were more popular. Interestingly, those with more dead ends (or cul de sacs) attracted fewer trips. Among all built environment variables tested, street structure offered the greatest predictive benefits, alongside jobs and population (densities and counts). For example, a 1-percent increase in the average number of 4-way intersections within a quarter-mile radius of the sampled households is estimated to increase the average household's non-motorized trip generation by 0.36%. A one-standard-deviation increase in the (mean) number of 4-way intersections at the average trip origin is estimated to increase the probabilities of bike and walk modes for interzonal home-based-work trips by 57% and 30%, respectively. In contrast, increasing the number of dead-ends at the origin by one standard deviation is estimated to decrease the probability of biking for both home-based-work and non-work trips by ~30%. These results underscore the importance of network density and connectivity for promoting non-motorized activity. The regional non-motorized travel (NMT) accessibility index (derived from the logsum of a destination choice model) also offers strong predictive value, with NMT counts rising by by 7% following a 1% increase in this variable -- if the drive alone accessibility index is held constant (along with all other variables, evaluated at their means). Similarly, household vehicle ownership is expected to fall by 0.36% with each percentage point increase in the NMT accessibility index, and walk probabilities rise by 26.9% following a one standard deviation increase in this index at the destination zone. A traveler's socio-economic attributes also have important impacts on NMT choices, with demographics typically serving as much stronger predictors of NMT choices than the built environment. For example, the elasticity of NMT trip generation with respect to a household's vehicle ownership count is estimated to be -0.52. Males and tose with drivers licenses are estimated to have 17% and 39% lower probabilities, respectively, of staying within their origin zone, relative to women and unlicensed adults (ceteris paribus). Non-motorized model choices also exhibit strong sensitivity to age and gender settings. Several of the regional variables developed in this work, and then used in the predictive models, are highly correlated. For example, bus stop and intersection densities are very high in job- and population-dense areas. For example, the correlation co-efficients between the bus stop density and 4-way intersection density is 0.805, between NMT and SOV AIs is 0.830 and between 4-way intersection density and NMT AI is 0.627. As a result, many variables are proxying for and/or competing with each other, as is common in models with many land use covariates, and it is difficult to quantify the exact impact of each of these variables. Nonetheless the models developed here provide valuable insight into the role of several new variables on non-motorized travel choices. Some final case study applications, moving all households to the downtown area (that has high accessibility indices and density), illustrate to what extent these revealed-data-based models will predict shifts toward and away from non-motorized trip-making. It appears that average household vehicle ownership level reduces to 0.57 from 1.89 (a 70% reduction) and average two-day NMT trip generation increases to 5.92 from 0.83 (an increase of more than 6 times). Such ranges are valuable to have in mind, when communities seek to reduce reliance on motorized travel by defining new built-environment contexts.
Book Synopsis Impact of Observed Travel and Charging Behavior, Simulated Workplace Charging Infrastructure, and Vehicle Design on PHEV Utility Factors (UF), Total Charge Depleting (CD) Driving and Time of Day (TOD) Grid Demand by : Jamie Davies-Shawhyde
Download or read book Impact of Observed Travel and Charging Behavior, Simulated Workplace Charging Infrastructure, and Vehicle Design on PHEV Utility Factors (UF), Total Charge Depleting (CD) Driving and Time of Day (TOD) Grid Demand written by Jamie Davies-Shawhyde and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Plug-in hybrid electric vehicles (PHEVs) can run on gasoline or grid electricity and have been widely touted as promising more future societal and environmental benefits than hybrid electric vehicles (HEVs). However, since the charging of PHEVs will place new loads on the electrical grid, how much and the time of day (TOD) at which users plug in their vehicles will have implications for electricity providers who must meet the additional electrical load required to charge a fleet of PHEVs. PHEV charging could place new burdens on existing electrical infrastructure (substations and transformers) and generating capacity. Information about consumers' charging behavior can help utilities and interested parties better plan for PHEVS in the marketplace. To date, analysts have made assumptions as to the design of PHEVs that will be purchased, and the travel and charging behavior of the future users. Furthermore, since PHEVs can run in charge depleting (CD) and charge sustaining (CS) modes there is uncertainty as to how much travel will be completed in each mode due to the variety of possible vehicle designs, access to charging infrastructure, and travel and charging behavior of PHEV users. Accounting for the amount of travel in each mode is crucial in order to accurately assess the fuel economy (FE) benefits, green house gas (GHG) emissions and costs of PHEVs. In 2001, the Society of Automotive Engineers (SAE) promulgated standard J2841 defining the utility factor (UF) as the percentage of travel that can be completed in CD mode for a PHEV fleet with a given CD range. As such, the SAE standard J2841 has a substantial influence on policies regarding PHEVs and their assumed benefits and costs, and has been used by analysts, industry, and policy makers to calculate PHEV corporate average fuel economy (CAFE), GHG emissions, operating costs and Zero Emission Vehicle (ZEV) credits. My analysis challenges J2841 by calculating the observed UF for a fleet of PHEVs driven by 25 Plausible Early Market (PEM) PHEV buyers in a demonstration and market research project. To estimate the potential effects on the UF of additional recharging infrastructure, I model a workplace charging scenario in which each of the 25 households recharges the PHEV at their workplace as well as at home. Lastly, hypothetical consumer designed PHEVs, solicited from each PEM household, are used to create and compare future market scenarios in which consumers are offered a wide variety of makes and body styles of PHEVs--thus simulating a plausible future market in which a variety of PHEVs are offered for sale. The results suggest that promoting "short range" PHEVs and focusing on popular vehicle-types, rather than upon achieving high CD ranges, could lead to greater total benefits from PHEVs in the early market, through more widespread adoption of PHEVs. Compared to SAE J2841, the observed UFs from the PEM demonstration data are 10 percentage points higher for PHEVs of up to 40 miles of CD range. At 40 miles CD range, J2841 stipulates a UF of 62%; I calculate a UF of 72% from the observed data. The increase in CD driving from adding simulated workplace charging varies by vehicle range, with the largest percentage point increases in CD driving occurring below 20 miles. Workplace charging changes the TOD distribution of power needed to charge a fleet of vehicles, producing a new maximum at 9:30am. The addition of workplace charging under the conditions modeled here does not change the evening peak power demand.
Book Synopsis Modeling and Managing Electric Vehicle Drivers' Travel Behavior in a Demand-Supply-Coupled Transportation System by : Yang Song
Download or read book Modeling and Managing Electric Vehicle Drivers' Travel Behavior in a Demand-Supply-Coupled Transportation System written by Yang Song and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The global shift towards electric vehicles (EVs) holds immense promise for mitigating greenhouse gas (GHG) emissions and advancing sustainable development goals. Nonetheless, the limited market penetration of EVs persists, primarily due to challenges in meeting the demand for replenishment compared to conventional internal combustion engine vehicles (ICEVs). The overarching goal of this dissertation is to develop mathematical models and a management framework for EV drivers' travel behaviors in a demand-supply-coupled transportation system, with the ultimate aim of facilitating the widespread adoption of EVs. By gaining deeper insights into and effectively managing various aspects of EV driver behaviors, such as charging preferences and route choices, the following benefits can be achieved: meeting the charging demands of EV drivers, optimizing the utilization of charging facility supply, and promoting the adoption of EVs as a preferred mode of travel. Firstly, the charging behavior of EV drivers is modeled based on the given charging facility supply. The existing research efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To fill the gap, an ensemble learning model based on gradient boosting is developed. A total of 18 features are defined and extracted from the multisource data, which cover information on drivers, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5 years of charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with an R square value of 0.54 and root mean square error of 0.14, both better than the two benchmark models, the multiple linear regression model and the random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users, or that are further from residential areas. Secondly, the provision of charging infrastructure is formulated as a demand management mechanism accounting for the underlying demand-supply coupled relationship. The existing studies treat each charging station as an independent entity and naively select the candidate locations with the highest individual usage rates. To address this issue, a two-stage learning-based demand-supply-coupled optimization model for the charging station location problem (CSLP) is proposed, aiming to incorporate the concept of EV charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station (CS) based on 15 defined features. Next, in stage two, a demand-supply-coupled CSLP model is developed with the objective of maximizing the total charging usage rates of both existing and newly selected charging stations. The proposed model is solved using a gradient-based stochastic spatial search algorithm. A case study using the same data as the first chapter is performed to test the effectiveness of the proposed model and algorithm. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches, namely the Greedy-Based Method and Neighbor-Swap-Based Method. Lastly, the routing behavior, as another aspect of EV driver travel behaviors, is modeled in a community charging setting. The existing research focuses on the EV traffic assignment under the scenario of corridor charging in a small-scale road network, ignoring the link interactions in community charging and path deviations in large-scale road networks. To tackle these challenges, an EV traffic assignment model for large-scale road networks with link interaction in community charging and with path deviations is proposed. First, the mathematical formulation for the EV traffic assignment model considering the interaction among road links connecting to the same CS is proposed, which is further proven to be equivalent to the user equilibrium (UE) condition. Then, a column-generation-based solution algorithm is developed to solve the model, facilitating the complex EV path deviations in a large-scale road network. The result of numerical examples shows that the proposed algorithm could converge in 0.025, 1.71, 4.73 and 91 seconds with a relative gap of no more than 0.0008 on the four testing networks, being the most accurate and fastest compared with the three benchmark algorithms, Frank-Wolfe algorithm, Interaction-Ignored algorithm, and Commercial-Solver-Based algorithm. The sensitivity analysis results show that the total travel cost and the total system dwelling time exhibit a negative correlation with charging supply while displaying a positive correlation with charging demand.
Book Synopsis Simulation Based Assessment of Plug-in Hybrid Electric Vehicle Behavior During Real-World 24-Hour Missions by : Tae-Kyung Lee
Download or read book Simulation Based Assessment of Plug-in Hybrid Electric Vehicle Behavior During Real-World 24-Hour Missions written by Tae-Kyung Lee and published by . This book was released on 2010 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
