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At least 325 records · Page 18

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DOE OSTI2019

The impact of reliable range estimation on battery electric vehicle feasibility

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range or when their journeys were unexpectedly extended. This paper quantifies the impact of reliable range estimation on BEV feasibility using GPS-tracked travel survey data, collected over an 18-month period (from November 2004 to April 2006) in the Seattle metropolitan area. BEV feasibility is quantified as the number of days when travel adaption is needed if a driver replaces a conventional gasoline vehicle (CGV) with a BEV. The distribution of BEV range is estimated based on the real-world fuel efficiency data. A driver is assumed to choose between using a BEV or a substitute gasoline vehicle, based on the cumulative prospect theory (CPT). BEV is considered feasible for a particular driver if he/she needs to use a substitute vehicle on less than 0.5% of the travel days. By varying the values of some CPT parameter, the percentage of BEV feasible vehicles could change from less than 5% to 25%. Additionally, the numerical results show that with a 50% reduction in the standard deviation and 50% increase in the mean of the BEV range distribution BEV feasibility increases from less than 5% of the sampled drivers to 30%.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Quantifying the Tangible Value of Public Electric Vehicle Charging Infrastructure

The lack of an extensive public recharging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Because charging infrastructure is likely to be underutilized during the early stages of market development, it is difficult for decision makers to decide how much to invest in public charging stations. Quantifying the value of public charging infrastructure to current and potential future owners of PEVs is essential for estimating the benefits of charging stations to current PEV owners and for predicting the impact on future PEV sales. This paper estimates consumers’ willingness to pay for public charging infrastructure in the context of utility maximization. The objective is to provide a method for valuing charging infrastructure that can inform investment decisions and be used in forecasting models to predict the impacts on future PEV sales. A basic theory of the tangible value of charging infrastructure is developed as a function of PEV type, range, recharging time and existing infrastructure. Existing simulation studies provide functional relationships that quantify the ability of charging infrastructure to enable additional miles of electrified travel. The enabled travel functions are used to predict impact of infrastructure deployment on incremental electrified travel for 1) plug-in hybrids and 2) intra-regional and 3) inter-regional travel by all-electric vehicles. The willingness to pay for increased electrified miles is derived from the willingness to pay for increased electric driving range, based on econometric studies of plug-in vehicle choice. The result is a set of three functions that can be used to calculate the marginal willingness-to-pay for public charging infrastructure as a function of vehicle attributes, existing charging infrastructure, energy prices and annual vehicle travel.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Inventory of Public Key Cryptography in US Electric Vehicle Charging

Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Key Performance Indicators for Vehicle Grid Integration

Electric vehicle (EV) sales account for a rapidly growing portion of the light-duty vehicle market and a portion of medium and heavy-duty fleet vehicles. However, in many locations, charging stations will require costly utility grid upgrades with long lead times. Today, there are a few methods of smart charge management (SCM) which can reduce the costs and wait times for electric vehicle supply equipment (EVSE) interconnection approvals, as well as reduce impacts of the charging stations on the grid and on EV driver transportation costs. It is crucial for the EV charging industry to understand the vehicle grid integration (VGI) requirements for EVSE to prevent adverse impacts from EVSE interconnections and assure that charging loads and interconnections are affordable.

33 - ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

The State of Electric Vehicle Adoption in Colorado for Multifamily versus Single-Family Dwellings: A Methodology for Quantifying Deviation from Parity

Given that electric vehicle adoption is well underway, the spatial distribution of electric vehicle owners by housing type—single-family or multifamily— shows whether parity (equal adoption rates) is being achieved or to what extent adoption by housing type is over or undersaturated (i.e., over- or under-adoption). We use a proprietary dataset of vehicle registrations with modeled housing type to analyze saturation ratios in Colorado in 2022. We found significant single-family oversaturation and multifamily undersaturation in 14% and 23% of ZIP codes, respectively, suggesting Colorado can still mitigate disparities in electric vehicle adoption by housing type through accessible vehicles and charging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Light-Duty Vehicle Choice Modeling and Transportation Decarbonization Analysis

Light-duty vehicles are the biggest contributor of carbon emissions in the transportation sector. This project uses the Automotive Deployment Options Projection Tool (ADOPT) to explore pathways to light-duty decarbonization through technology improvements being researched by the Department of Energies Vehicle Technology Office. It includes sensitivities to market conditions, such as proposed vehicle purchase incentives, and changes to the Corporate Average Fuel Economy and Greenhouse Gas regulations. The results suggest that achieving the Vehicle Technology Office's research goals will lead to one third of the annual carbon emissions by 2050, primarily through vehicle electrification.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2022

Supporting Electric Vehicle Supply Equipment Deployment

The U.S. Department of Energy (DOE) Federal Energy Management Program (FEMP) helps federal agencies electrify their fleets and support the deployment of charging infrastructure. To assist agencies with the transition to zero-emission vehicles (ZEVs), including battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), FEMP offers technical guidance on electric vehicle supply equipment (EVSE) installations and site-specific planning through on-site and virtual EVSE Tiger Teams.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Robust vehicle routing under uncertainty via branch-price-and-cut

Here, this paper contemplates how branch-price-and-cut solvers can be employed along with the robust optimization paradigm to address parametric uncertainty in the context of vehicle routing problems. In this setting, given postulated uncertainty sets for customer demands and vehicle travel times, one aims to identify a set of cost-effective routes for vehicles to traverse, such that the vehicle capacities and customer time window constraints are respected under any anticipated demand and travel time realization, respectively. To tackle such problems, we propose a novel approach that combines cutting-plane techniques with an advanced branch-price-and-cut algorithm. Specifically, we use deterministic pricing procedures to generate "partially robust" vehicle routes and then utilize robust versions of rounded capacity inequalities and infeasible path elimination constraints to guarantee complete robust feasibility of routing designs against demand and travel time uncertainty. In contrast to recent approaches that modify the pricing algorithm, our approach is both modular and versatile. It permits the use of advanced branch-price-and-cut technologies without significant modification, while it can admit a variety of uncertainty sets that are commonly used in robust optimization but could not be previously employed in a branch-price-and-cut setting.

42 ENGINEERING↗

DOE OSTI2023

Characterization of recent wild pig-vehicle collisions in Georgia, USA

Vehicle collisions with wild pigs (Sus scrofa) are reported almost everywhere this species is found. However, this is one of the least studied and characterized forms of damage that these invasive animals cause in the United States (U.S.). Here, we analyzed 518 wild pig-vehicle collisions (WPVCs) that took place statewide in Georgia between 2015 and 2021. From that dataset, we analyzed several parameters in order to better understand and characterize these accidents on a scale that had previously not been done in the U.S. Wild pig-vehicle collisions were reported from 105 out of the 159 counties in Georgia, increasing in number annually over the seven-year period. WPVCs were most likely to occur in the fall (37%). A duration weighted time of day analysis showed that WPVCs were most frequent at dusk. Most (97%) reported accidents were caused by live wild pigs, with the remainder being due to collisions with already dead or road-killed pigs. Most (86%) collisions involved a single wild pig while the remainder occurred with two or more pigs. Collisions occurred mostly with passenger cars on dry, straight, and level two-lane blacktop roads under dark, unlighted conditions. Nine percent of the vehicle accidents involving wild pigs resulted in injuries to the drivers and passengers. Some of the most severe injuries reported were caused by swerving to avoid striking wild pigs. No human fatalities were recorded due to these accidents. Wild pig-vehicle collisions are costly and dangerous and should be closely monitored and mitigated by the agencies responsible for motorist safety, transportation infrastructure, and wildlife management.

Georgia↗

DOE OSTI2020

Range cost-effectiveness of plug-in electric vehicle for heterogeneous consumers: An expanded total ownership cost approach

Plug-in electric vehicles (PEV) appears to have sales momentum in major personal vehicle markets but are still at the early market stage. Opportunities to accelerate PEV adoption can be discovered through comprehensive total cost of ownership (TCO) analysis. Understanding the cost-effective electric ranges of PEVs for consumers, manufacturers, and the society is critical for any discussion of PEV mass markets. This study expanded the traditional TCO approach by (1) fully considering heterogeneous consumer driving patterns, (2) quantifying the charging inconvenience and range anxiety cost of battery-electric vehicles (BEVs), and (3) monetizing both tangible and intangible PEV policies. Uncertainties were handled through Monte Carlo simulation. The results suggest that BEVs with an electric range of 250–350 km have the lowest TCO in cities with government-enacted purchase limitations, and internal combustion engine vehicles (ICEVs) have the lowest TCO in cities without purchase limitations, even when considering PEV subsidies. The lowest TCO for some consumer groups is obtained by BEVs with an electric range of 400–450 km, especially in northern China, where the weather is colder. The cost-effective all-electric range for BEVs in each city in 2025 will decrease due to improved battery performance in cold environments and an expanded charging infrastructure. Finally, based on TCO, plug-in hybrid electric vehicles (PHEVs) are currently more suitable for drivers with a high average daily mileage or a large mileage variance. However, by 2025, BEVs with a long driving range may become a more cost-effective choice for these drivers.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Impact of battery cell imbalance on electric vehicle range

Due to manufacturing variation, battery cells often possess heterogeneous characteristics, leading to battery state-of-charge variation in real-time. Since the lowest cell state-of-charge determines the useful life of battery pack, such variation can negatively impact the battery performance and electric vehicles range. Existing research has been focused on control design to mitigate cell imbalance. However, it is yet unclear how much impacts the cell imbalance can have on electric vehicle range. This paper closes this knowledge gap by using a simulation environment consisting of real-world driving speed data, vehicle longitudinal control, propulsion and vehicle dynamics, and cell level battery modeling. In particular, each battery cell is modeled as an equivalent circuit model, and variations among cell parameters are introduced to assess their impact on electric vehicles range and to identify the most influential parameter variations. Simulation results and analysis can be used to assist balancing control design and to benchmark control performance.

25 ENERGY STORAGE↗

DOE OSTI2022

Grid impact analysis using controller-hardware-in-the-loop for high-power vehicle charging stations

A controller-hardware-in-the-loop (CHIL) architecture for the evaluation of grid impacts arising from high-power vehicle charging stations is presented in this paper. Unlike simulation-based studies, the proposed method can be used to capture the interactions of the grid and the charging load along with charger controllers in real time. The proposed method can be used to evaluate the impact of charging load on the grid in terms of voltage variations and line congestion. The proposed CHIL platform allows for de-risking the vehicle charging station deployment by simulating the complex interactions among all the components of a vehicle charging station - i.e., the grid, vehicle, and charger controller - in a realistic manner before using the charging station in a grid. Further, the proposed CHIL approach can be used to evaluate the voltage regulation causalities of the vehicle charging station. Experimental results are presented in the paper to illustrate the applicability of the proposed method in a laboratory environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Additionally, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

42 ENGINEERING↗

DOE OSTI2025

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit

Here, this paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

Autonomie↗

DOE OSTI2024

Electric light-duty vehicles have decarbonization potential but may not reduce other environmental problems

Electric vehicles are promoted as ‘clean’ technologies and offer promising reductions in transportation emissions. Nevertheless, their environmental benefits critically depend on the local electricity grid mix and the type of emission being considered. Here, we conduct a comparative life cycle assessment of the four dominant light-duty vehicle categories at both the global scale and in three representative countries: Norway, the US, and China. By analyzing different environmental indicators, particularly global warming potential and respiratory effects, and quantifying related parametric uncertainties, we reveal that the advantages of electric vehicles vary across these regions and across environmental impact types. While electric vehicles offer considerable decarbonization potential as the grid mix becomes cleaner, they might not mitigate other environmental impacts, such as increased respiratory effects on rural, low-income communities. Our results support stakeholders in identifying environmentally friendly vehicle and policy options while considering multiple factors, and emphasize the importance of tailored approaches over one-size-fits-all solutions in sustainable transportation.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Enabling cooperative adaptive cruise control on strings of vehicles with heterogeneous dynamics and powertrains

Recent studies have shown that positive impact of Cooperative Adaptive Cruise Control (CACC) can only be guaranteed as market penetration rate increases. Removing the string homogeneity constraint is essential to encourage widespread adoption. In this work, a hierarchical architecture is proposed to enable CACC on vehicles with not only mixed dynamics but also different powertrain types. A low-level layer deals with the vehicle and powertrain dynamics to provide accurate and consistent reference speed tracking response. The high-level layer uses: (1) a Linear Parameter Varying feedback system to provide loop stability, robustness and enforce a variable time gap policy and (2) a feedforward system that processes Vehicle-to-Vehicle information to enhance string stability and response bandwidth, by dealing with the string heterogeneity. A gap management strategy is built on top of the CACC architecture to handle gap setting changes or cut-in/out situations, via a dynamics constrained time gap trajectory planning algorithm. We report the proposed work has been designed, developed and validated on three different real passenger vehicles on public highways and test tracks, showing the potential of the proposed algorithm to enable robust string stable CACC, despite the different dynamics and powertrains considered.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Optimizing Vehicle Fleet and Assignment for Concentrating Solar Power Plant Heliostat Washing

Concentrating solar power central-receiver plants use thousands of sun-tracking mirrors, i.e., heliostats, to reflect sunlight to a central receiver, which collects and uses the heat to generate electricity. Over time, soiling reduces the reflectivity of the heliostats and, therefore, the efficiency of the system. Current industry practice sends vehicles to wash heliostats in an ad hoc fashion. We present a mixed-integer nonlinear program that determines wash vehicle fleet size, mix, and assignment of wash crews to heliostats to minimize the sum of (i) the revenues lost due to heliostat soiling, (ii) the costs of hiring wash crews and operating the vehicles, and (iii) the costs of purchasing wash vehicles. We establish conditions for convexity of the objective function, and then propose a decomposition method that enables near-optimal solutions to the wash vehicle fleet sizing and assignment problem on the order of a couple of minutes. Furthermore, these solutions yield hundreds of thousands of dollars in savings per year over current industry practices.

14 SOLAR ENERGY↗