Overestimated forecasts of the impact of new policy, which over-predict policy success, are a well-known problem. Studying the effects of forecasting methods on potential biases may help modelers, planners and policy makers better use the forecasting tools. This paper addresses overestimation of telecommuting as a travel demand management (TDM) policy. The research hypothesis underlying this study posits that overestimates are virtually inevitable in forecasting the effect of new policies that aim to change travel behavior, but these biases eventually decline over time. The sources of overestimated forecast are the prediction tools used, and the ways in which modelers use these tools. The sources of the reduction in overestimation are the changes made to the modeling tools results from knowledge and data gained over time.

Car buyers in California have the choice of several types of alternative fuel vehicles (AFVs) including battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs). FCVs offer a different ownership proposition compared to BEVs, mostly relating to their refueling style. This policy brief summarizing findings from the study which investigated FCV buyers in California and compared them to BEV owners. The hope is to understand why some households choose a FCV rather than a BEV.

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In the US, the market share of plug-in electric vehicles (PEVs)—including battery electric and plug-in hybrid electric vehicles—has been rapidly increasing as a variety of new PEVs have been introduced. Knowing where PEV users are located is important to ensure that electric vehicle charging infrastructure is installed in areas where it is needed. Information on PEV location can also inform electricity supply planning to prepare for a future with higher PEV adoption. Previous studies have looked at the spatial distribution of new PEVs but not of used PEVs. Yet these spatial distributions will likely differ because the buyers of used PEVs have different characteristics than new PEV buyers. Therefore, planning charging infrastructure and electricity supply based solely on new PEV data may not serve both new and used PEV buyers. Policies developed to support drivers of used PEVs may ultimately attract a broader group of people into the PEV market, as used vehicles are less expensive than new ones. Researchers at the University of California, Davis used aggregated data at the zip code level to understand where buyers of second-hand PEVs are located, and to explore differences in the location and characteristics of regions with more original owners vs. second owners of PEVs. This policy brief summarizes the findings from that research and provides policy implications.

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Total cost of ownership (TCO) analysis assesses the cost competitiveness of plug-in electric vehicles (PEVs) and internal combustion engine vehicles (ICEVs). The TCO literature characterizes an average driver to identify a future parity point where the ownership cost of a PEV is equal to or lower than an ICEV. This research had taken a different approach for TCO analysis, estimating costs of actual PEV early adopters in California between 2011 and 2020 using multi-year survey responses that provided valuable insights regarding vehicle purchase choice, driving and charging behavior, and sociodemographic attributes.The TCO comparative analysis was done by vehicle segments (compact, mid-size, etc.), using selected models from the year 2018 and included incentives. The results show that the higher the segment was, the lower the annual mileage required to achieve a comparative TCO. For the mid-luxury segment, cost parity was achieved with only 10,100 annual miles, while for the subcompact segment, parity was reached when driving over 27,000 miles annually. As PEVs enter the mass market these days, these findings demonstrate the importance of incentive programs that are structured to benefit specific market segments that need them the most, rather than applying subsidies across all models, as done past decade to encourage early adoption.

California has set an ambitious goal of 100% zero-emission vehicle sales by 2035. Most consumer research to date has focused on understanding the factors influencing the initial purchase of plug-in electric vehicles (PEVs). But for the market introduction of PEVs, which include both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), to be successful, subsequent vehicle purchases by initial adopters need to continue to be PEVs rather than conventional vehicles. Discontinuance, the act of abandoning a new technology after once being an adopter, could make achieving California’s goal more challenging.

Researchers at the University of California, Davis surveyed California PEV buyers two to seven years after they first purchased their electric vehicle to understand whether they have continued to choose PEVs with subsequent purchases, and if not, what factors may have led to their discontinuance of the technology. This policy brief summarizes the findings from that research and provides policy implications.

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For the market introduction of electric vehicles to be successful, first-time adopters need to make continual purchases of the vehicles. Discontinuance, the act of abandoning a new technology after once being an adopter, has implications for market growth and could prevent electric vehicles from ever reaching 100% market share. Using results from five surveys of electric vehicle owners, the researchers examine discontinuance among battery electric and plug-in hybrid electric vehicle adopters. In this sample, discontinuance occurs at a rate of 21% for plug-in hybrid adopters and 19% for battery electric vehicle adopters. They show that discontinuance is related to dissatisfaction with convenience of charging, owning household vehicles with lower efficiencies, being a later adopter of PEVs, not having Level 2 (220V) charging from home, and not being male. Despite consumers overcoming initial barriers of PEVs, it appears some barriers, notably their refueling style, resurface during ownership and eventually become a barrier to continuing with PEV ownership.

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This dissertation aims to explore some of the key barriers to realizing the full emission reduction potential of Plug-in Electric Vehicles (PEVs). Specifically, it explores the tradeoffs between Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) at reducing tailpipe emissions in the presence of an imperfect Electric Vehicle (EV) charging network. BEVs are true Zero Emission Vehicles (ZEVs) since they rely solely on energy from a battery (i.e., a rechargeable energy storage system) for propulsion, emitting no tailpipe emissions. In contrast, PHEVs can propel themselves using a combination of battery and internal combustion engine (ICE) energy. As such, driver behavior that determine the extent to which PHEVs’ electric range is used, have a strong influence on their energy use and emission potential. The first few chapters of this dissertation specifically explore the impact of the interaction between driver behavior and technical vehicle parameters on the energy consumption and Green House Gas (GHG) emissions of PHEVs in order to inform policy about the true emission potential of these low emission vehicles. Chapter 2 presents a study that aims to characterize the engine start activity profiles and emission potential of various PHEV models by examining the characteristics associated with engine starts, identifying the travel conditions that trigger engine starts, and determining the frequency of different types of starts. The study ultimately finds that long range PHEVs with high battery capacity such as the Chevrolet Volt are ideal for both curbing start emissions via initializing few engine starts and maximizing fuel displacement. Chapter 3 presents two studies that aim to understand the motivations and implications of driver mode, user-selectable drivetrain configuration setting, usage in PHEVs. In addition to comprehensively defining and classifying various drive modes, the first study examines the motivations for drive mode usage using a survey of over 26,000 PEV drivers in California. The second study quantifies the energy use and emission impacts of drive mode usage using on-road vehicle data from 81 Chevy Volts driven in California. Since BEVs aren’t equipped with ICEs, they are far superior to PHEVs at curbing tailpipe emissions. However, given the vehicles are solely powered by electricity, the adoption and acceptance of BEVs is tightly coupled with the quality of the EV charging infrastructure. As such, the scarcity of reliable and functional EV charging stations presents a significant barrier to the widespread adoption of BEVs. This dissertation aims to complement and expand the limited literature on EV charging reliability by examining the impact of EV charger reliability on BEV driver experience and developing a tool to help charging networks effectively meet impending reliability standards. Chapter 4 presents a study that focuses on understanding the impact of EV charger reliability on driver experience. It uses real-world EV charging data to simulate the level of disruption that would’ve occurred to EV drivers had their successful charging sessions been unsuccessful. Additionally, it quantifies how many charging sessions were actually unsuccessful and qualifies how disruptive those unsuccessful charging sessions were to drivers. By quantifying and qualifying the level of disruption associated with both real and hypothetical charge failures, it finds that EV chargers are not all equally important to EV drivers, highlighting the need for more nuanced charging reliability standards to more effectively meet consumer charging needs. Chapter 5 develops a tool enabling EV charging service providers to swiftly detect charge failures that cannot be detected by standard monitoring protocols. By analyzing habitual charging patterns of EV drivers, the tool identifies unexpected gaps in charger usage, indicating potential charger faults. The tool incorporates two anomaly detection models: a naive probability distribution-based technique and a LSTM for complex pattern modeling. Depending on the tool’s preferred confidence level, CPOs could’ve detected potential charging faults 1.5 to 3 times faster with the naive method and 1.5 to 2.4 times faster with the LSTM method.   The Risk and Resilience of Plug-in Vehicles in the Presence of an Imperfect Charging Network