California's Zero Emission Vehicle (ZEV) mandate targets all new Light Duty Vehicle (LDV) sales to be ZEVs by 2035. However, the current charging infrastructure is not well-developed in California, primarily serving households with home charging setups and leaving a noticeable gap in public charging facilities. This gap is seen as a significant barrier to Battery Electric Vehicle (BEV) adoption within California. This report explores driver charging behavior and their preference for public DC fast charging (DCFC), drawing on Stated Preference (SP) choice experiment data from a survey of 1,102 Plug-in Electric Vehicle (PEV) owners across California.
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To meet zero-emission vehicle targets, people will have to adopt electric vehicles and convert their entire fleets. In the United States and California, most households own two or more vehicles; most of these households will need to switch their traditional vehicles for plug-in electric vehicles (PEVs). However, most of the research on PEV adoption has focused on people acquiring their first PEV. This work is the first to examine households’ decision to maintain at least two PEVs in their household fleets. Utilizing a multi-year survey of PEV adopters between 2012 and 2020, 3,039 respondents who acquired a vehicle after obtaining an initial PEV are identified. Respondents are divided in two groups: those who reverted to an internal combustion engine vehicle (Single PEV) and those who added an additional PEV (Multi PEV). Modelling the groups using binary logistic regression, several factors that differentiate Single from Multi PEV households are identified. Compared to Single PEV, Multi PEV households are more likely to have owned previous PEVs, live in detached single-family homes with solar, own an SUV prior to their initial PEV, purchase a Tesla for their initial PEV, and use the initial PEV for commuting.
This study delves into the energy and emissions impacts of Shared Autonomous and Electric Vehicles (SAEVs) on disadvantaged communities in California. It explores the intersection of evolving transportation technologies—electric, autonomous, and shared mobility—and their implications for equity, energy consumption, and emissions. Through high-resolution spatial and temporalanalyses, this research evaluates the distribution of benefits and costs of SAEVs across diverse populations, incorporatingenvironmental justice principles. Our quantitative findings reveal that electrification of the vehicle fleet leads to a 63% to 71% decrease in CO2 emissions even with the current grid mix, and up to 84%-87% under a decarbonized grid with regular charging. The introduction of smart charging further enhances these benefits, resulting in a 93.5% - 95% reduction in CO2 emissions. However, the distribution of these air quality benefits is uneven, with disadvantaged communities experiencing approximately 15% less benefits compared to more advantaged areas. The study emphasizes the critical role of vehicle electrification and grid decarbonization in emissions reduction, and highlights the need for policies ensuring equitable distribution of SAEV benefits to promote sustainable and inclusive mobility.
The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, the researchers consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. The project examines two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a “Small Vehicles” scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility vehicles (SUVs), or vans and a “Large Vehicles” scenario with 4 million of each category. The authors find that an electrification scenario with more electric trucks and SUVs serves to distribute electrified households more evenly throughout the state, shifting them from urban to rural counties, while there is little impact on home-charging access.
To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid.
Vehicle electrification has attracted strong policy support in California due to its air quality and climate benefits from adoption. However, it is unclear whether these benefits are equitable across the state’s sensitive populations and socioeconomic groups and whether disadvantaged communities are able to take advantage of the emission savings and associated health benefits of electric vehicle (EV) adoption. In this study, we analyze the statewide health impacts from the reduction of on-road emissions reduction (from reducing gasoline powered cars) and the increase in power plant emissions (from EV charging) across disadvantaged communities (DACs) detected by using the environmental justice screening tool CalEnviroScreen. The results indicate that EV adoption will reduce statewide primary PM2.5 emissions by 24.02-25.05 kilotonnes and CO2 emissions by 1,223-1,255 megatonnes through 2045, and the overall monetized emission-related health benefits from decreased mortality and morbidity can be 2.52-2.76 billion dollars overall. However, the average per capita per year air pollution benefit in DACs is about $1.60 lower than that in the least 10% vulnerable communities in 2020, and this disparity expands to over $31 per capita per year in 2045, indicating that the benefits overlook some of the state's most vulnerable population, and suggesting clear distributive and equity impacts of existing EV support policies. This study contributes to our growing understanding of environmental justice rising from vehicle electrification, underscoring the need for policy frameworks that create a more equitable transportation system.
The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body- and fuel-type to project future VMT changes and mobile source emission levels. Leveraging the 2019 California Vehicle Survey data, this report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. The model results suggest the important effects of household demographics, residence location, and built environment factors on vehicle body type and powertrain choice and usage. Further the predictions associated with changes inbuilt environment factors like population density can inform the design of land-use and transportation policies to influence household vehicle holdings and usage that can in turn impact travel demand and air quality issues in California.
This study solicited information directly from decision-makers in private businesses operating fleets of medium- and heavy-duty trucks in California via interviews and pre-interview questionnaires. Additional interviews were conducted with truck manufacturers, consultants and other businesses providing services to the freight industry including leasing and auction. All these data were collected in 2021 and 2022. Fleet decision-makers describe what determines when and why they acquire and retire trucks and how they use those determinants. The purpose is to better understand vehicle turnover in the trucking sector. Direct contact with fleet decision-makers was preceded by a review of relevant literatures. This review helped in the design of joint questionnaires and interview protocols. Results are presented as 1) a set of determinants (internal to each fleet, external, and linking internal to external), 2) a typology based on decision-making structure, adaptation, and complexity, 3) case studies of decision-making types, 4) generalizations across fleets, and 5) extension to fleet consideration of alternative fuel trucks. One overarching conclusion is drawn: fleet truck turnover behavior varies widely—our highest-level abstraction—the typology—results in more than 20 types among 90 fleets allowing that some types involve mixed types of structure, adaptation, and/or complexity. Few fleets’ decision-making conforms to the commonly assumed model of total cost of ownership; many more do not. This report describes the varied ways fleets acquire and retire trucks, extends this to understand how this variety is already affecting freight fleets’ consideration of alternative fuel trucks, and poses questions as to how understanding this variety aids in promotion of zero-emission trucks.
