The Caltrans ZEV 30-30 project is established to support California's goal of deploying five million ZEVs on the road by 2030, focusing on filling gaps in the State Highway System's corridor ZEV charging network. The reliability of DC Fast Charging (DCFC) infrastructure along these corridors is essential. Unreliable chargers erode driver confidence and undermine the transition to zero-emission transportation. Charging Station Operators (CSOs) currently rely on conventional monitoring protocols, primarily the Open Charge Point Protocol (OCPP), to detect charger failures. While OCPP-based monitoring is effective for detecting most electrical and software failures, it cannot identify a broad class of faults arising from mechanical damage, physical obstruction, network communication outages, or logistical barriers. [1, 2] These hidden issues persist until an EV driver encounters the faulty charger and reports the problem, leading to delayed fault resolution and degraded consumer experience.This report presents a predictive anomaly detection tool developed under Caltrans Agreement No. 65A1188 that enables Charging Station Operators to detect hidden charging faults by analyzing habitual EV driver usage patterns. The tool incorporates two anomaly detection models: a naïve probability distribution-based technique and a Long Short-Term Memory (LSTM) autoencoder.
This research brief explores factors influencing consumers decision to buy an electric vehicle (EV). The brief also explores what areas of the EV ecosystem EV owners think need improvement. Results come from interviews conducted with EV owners across the US. Following the interviews transcripts were thematically coded to extract common themes. Overall, we find consumers decisions to purchase an EV are influenced by functional or economic factors, such as refueling cost or purchase price. Emotional factors in the form of environmentalism played a role, but less so than previous studies. Desired improvements to EVs were mostly related to charging infrastructure, including improvements to infrastructure availability, charging speed, reliability, and other issues. Improvements to driving range were also desired.
This study examines the used plug-in electric vehicle market in California through an integrated analysis of Department of Motor Vehicles household registrations from 2023, S&P Global interstate transfer data from 2016 to 2023, and a statewide survey of vehicle owners with approximately 3,396 respondents. This includes understanding socio-economic, demographic, geographic, and behavior of not only buyers of used PEVs but also buyers of new and used internal combustion engine (ICE) vehicles. The analysis provides empirical evidence on consumer behavior, market dynamics, and barriers to adoption that can inform policy decisions aimed at expanding plug in electric vehicle access across California’s population.
Second-hand (SH) vehicle imports from the US comprise nearly 20 percent of the 30 million light-duty vehicles (LDV) currently registered in Mexico. As demand for electric vehicles (EVs) in Mexico grows and the share of EVs in the US fleet continues to increase, the SH EV market in Mexico is likely to start developing, introducing new challenges for vehicle lifetime and end-of-life (EoL) management needs. Using system dynamics modeling, researchers at the University of California, Davis, developed scenarios to project future trends in EV adoption and SH vehicle trade flows in Mexico. Results indicate potential synergies with respect to market timing, but also a risk of disproportionate burdens from spent batteries in Mexico, since used EVs have less remaining battery life and thus generate spent batteries more quickly than a new EV. This trade of SH EVs between Mexico and the US should be managed bilaterally, ensuring that imports to the country deliver sufficiently long operational lives, and exploring opportunities to set up regional battery recycling systems to recover critical minerals, so that the burden of EoL managements do not outweigh the benefits of affordable EVs.
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.
View the NCST Project Webpage
To meet zero-emission vehicle targets, consumers 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.
