blog
California aims to dramatically increase its electric vehicle (EV) charging infrastructure, but faces significant challenges. A new business model called charging-as-a-service (CaaS) could help overcome these obstacles and support the state’s ambitious climate goals.
research report |
March 1, 2025
policy brief
Access to electric vehicle (EV) charging infrastructure is critical to advancing California’s EV adoption goals. The California Energy Commission has projected the state needs “nearly 1.2 million” chargers by 2030 “to meet the fueling demands of 7.5 million passenger plug-in electric vehicles.” Currently, California has about 152,000 publicly available EV chargers.
Innovative asset ownership models, like charging-as-a-service (CaaS), could help overcome some of the barriers to deploying and maintaining charging infrastructure. For example, CaaS providers could procure, install, maintain, and replace charging equipment for subscription customers. To better understand how CaaS solutions could expand EV use and charging access, this researchers conducted semi-structured interviews with 13 CaaS companies, electric utilities, and customers to identify the perceptions, challenges, and opportunities of the CaaS business model in addressing charging station needs in California.
research report |
March 1, 2025
research report
Charging-as-a-Service (CaaS) is an innovative electric vehicle (EV) charging station model that allows customers access to EV chargers through a contract with a provider responsible for design, deployment, operations, and maintenance. Little is known about the motivations and experiences of stakeholders involved in CaaS operations, including providers, electric utilities, and customers. A grey literature review identified CaaS services, provider-described benefits, and utility-provided CaaS and charging services. Then, semi-structured interviews were conducted with 13 stakeholders to identify critical themes on interactions between stakeholders and the perceptions, challenges, and opportunities of the CaaS business model in addressing charging station needs in California. CaaS may have structural benefits to customer-owned chargers and could improve charger reliability, provide scalable solutions, and reduce customer fatigue with EV charging deployment. However, CaaS faces the same challenges present in the broader charging industry. The findings in this study can guide policymakers in supporting maintenance-related workforce development and streamlining and crafting EV charging infrastructure-informed subsidy programs. Additionally, stakeholders recommend municipal-led EV infrastructure planning and funding for chargers in disadvantaged communities. These interviews clarify the role of CaaS within the EV charging industry and confirm the need for engaged policymaker support to clear roadblocks, support investment, and educate customers about decision-making, which benefits all EV charging stakeholders.
research report
Public transit ridership has declined in major US cities over the past decade. Integrating traditional fixed-route transit with flexible microtransit has been proposed to enhance ridership, mobility, accessibility, and sustainability. This project surveyed California transit agencies on their microtransit services to identify challenges to integrating them with fixed-route services. An agent-based model combining the two modes of transit was developed to evaluate different operational designs. FleetPy, an open-source simulation tool, modeled microtransit dynamics. The study examined design impacts, such as fixed route headways and microtransit fleet size, in downtown San Diego and Lemon Grove, California. Results showed that while microtransit reduces fixed-route ridership and requires higher subsidies, it significantly boosts job accessibility.
policy brief |
August 1, 2024
Phd Dissertation
Auction based market clearing mechanisms are widely accepted for conducting business-to-business transactions. This dissertation focuses on the development of auction mechanism decision tools for freight transportation contract procurement. The dissertation categorizes the problems in freight procurement auctions arising in both spot markets and long term markets. Spot markets are widely employed over the Internet using standard classic auctions. For long-term markets, large shippers (typically manufacturing companies or retailers) have begun to use combinatorial auctions to procure services from trucking companies and logistics services providers. Combinatorial auctions involve very difficult optimization problems both for shippers and carriers. In the US truckload market few carriers have the technical sophistication to develop bids for combinatorial auctions. To address this problem we look at a different auction scheme termed a unit auction, where the shipper can exploit the economies of scope in the network and give the carriers the chance to bid on pre-defined packages similar to ‘lotting’ in supply chain procurement. The problems in developing contract allocations, called the winner determination problem, are computationally complex and large-scale. Hence the development of good heuristics is of utmost importance. Shippers have non-price business constraints, which must be included in the winner determination problems to closely match shipper business objectives. We develop winner determination problem formulations incorporating the non-price business constraints and develop Lagrangian based optimization methods and greedy approximation algorithms for both unit auctions and combinatorial auctions. Extensive empirical results are provided to test the performance of the heuristics against a standard integer-programming solver. Bidding in auctions from the carrier’s perspective is complicated as it involves taking into account the competitive behavior of other carriers and a carrier’s difficult network optimization problems. We develop bidding strategies for carriers in spot markets using concepts from economic auction theory. For long-term market bidding, we study the effects of demand uncertainty, competitive behavior, carrier network synergies and strategic pricing, and shipper’s winner determination problems on carrier bidding using optimization-based simulation analysis.
Phd Dissertation
policy brief
The rapid growth in freight transportation, particularly heavy-duty trucks, poses significant environmental and public health challenges for communities near major ports and freeways. In areas such as those near the Port of Los Angeles and the I-710 corridor, communities are exposed to elevated levels of air pollution, noise pollution, and associated health risks. Traditional traffic data collection methods primarily concentrate on gathering traffic volume data for freeway segments or smaller areas, often overlooking heavy-duty vehicles across roadway networks and in local communities.
To better understand the environmental impact and spatial distribution of heavy-duty truck traffic, this research employed a deep learning approach to analyze satellite imagery and publicly accessible spatial data. This approach allowed identification and categorization of heavy-duty trucks and shipping containers along critical freight routes and analysis of impacts on adjacent communities.
journal article preprint |
August 29, 2024
Preprint Journal Article
Insufficient charging/fueling infrastructure poses a major challenge to achieving U.S. policy goals for transitioning the heavy-duty vehicle (HDV) sector to zero-emission vehicles. Addressing the infrastructure needs of HDV fleet operators, who are key demand-side stakeholders, is crucial for developing effective solutions and strategies. This study investigates these needs through a fleet survey of California’s drayage sector, focusing on battery electric trucks. Key aspects examined include preferences for charging locations, access types, charging duration, time-of-day for charging, and innovative solutions like Truck-as-a-Service. Analyzing responses from 53 companies with varying fleet sizes, annual revenues, and operational characteristics, the study employed a comprehensive exploratory approach, utilizing descriptive analysis, thematic analysis, and hypothesis testing. Findings reveal that while most fleets preferred on-site charging, about a quarter, primarily smaller fleets with five or fewer trucks, preferred both on-site and off-site options. Private access was often favored for on-site facilities, though some respondents recognized the benefits of shared access for expanding operational coverage. The study also identified a need for faster charging solutions at both off-site and on-site locations, particularly for long-haul or mixed operations. Time-of-day preferences varied widely, driven by the need for efficient operations. Furthermore, a small proportion of participating fleets preferred Truck-as-a-Service over traditional procurement, predominantly among smaller fleets or those with lower revenues. The comprehensive research findings contribute to a deeper understanding of charging infrastructure needs and offer practical insights for policy practitioners and industry stakeholders committed to advancing zero-emission infrastructure.
published journal article
California possesses multiple major freight gateway and logistics facilities that serve both the state and the entire U.S. But the economic, environmental, and local community impacts of trucks, especially heavy-duty trucks that are currently essential to our supply chains and freight transportation system remain poorly measured due to the lack of comprehensive and detailed truck activity data. This paper describes the pilot implementation of the real-time, scalable, and cost-efficient Freight Mobility Living Laboratory (FML2). This system provides truck characterizations across multiple attributes, such as truck body types, axle-based and Gross Vehicle Weight Rating (GVWR)-based classification and is currently deployed at 30 detection locations in Southern California along major freight corridors to support freight modeling and analysis needs. This paper details the design of the FML2 from edge data processing, predictive model development, communication architecture, and backend data storage to the real-time data dashboard to visualize the classification results. Three case studies have been presented at the end of the paper to demonstrate the potential of FML2 for use by both researchers and practitioners to gain further insights on truck activities.
policy brief
California aims to replace gasoline and diesel light-duty vehicles (LDVs) with zero-emission LDVs, many of which will be plug-in battery electric vehicles (BEVs) and achieve 100% zero-carbon electricity by 2045. Large-scale plug-in BEV deployment will substantially increase electricity demand, particularly during peak hours (4:00pm to 9:00pm) when renewable energy is in short supply. Popular strategies for charging BEVs with electricity produced from renewable energy include smart charging and creating more energy storage that soaks up renewable energy during the day and dispenses it later when needed. These strategies, however, may not be enough. Consumer acceptance limits smart charging, and increased energy storage capacity is expensive. Another potential strategy involves lowering the overall demand for electricity by shifting BEV trips to electric-powered bicycles (e-bikes). While e-bikes cannot entirely replace BEV trips, they are ideal for short trips (five miles or less). Currently, 64% of US vehicle trips fall into the short trip category. Using synthetic travel pattern data from the San Diego region, we quantified the electric grid cost savings of shifting future BEV trips to e-bikes. For our analysis, we determined the passenger LDV trips that e-bikes could potentially replace. To provide an upper bound on replaceable trips, we considered trips that met the following criteria: LDV trips within home-based tours (a sequence of trips starting and ending at the home location) made by no more than two household members (between 16 and 70 years old), with less than five stops, under four hours in travel duration, and with individual trip distances up to seven miles long. We also created three scenarios that differ in terms of the tour purposes:
• Scenario 1: All purposes (e.g., work, recreation, eating out, etc.) except escort (i.e., transporting someone else to their activity) and shopping tours
• Scenario 2: All purposes except escort tours
• Scenario 3: All purposes