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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Medium- and Heavy-Duty Vehicle Electrification: An Assessment of Technology and Knowledge Gaps

The objective of this assessment was to leverage current medium- and heavy-duty vehicle (MHDV) data and information to evaluate the state of commercial vehicle electrification technologies, including the following: 1. Assessment and inventory of current MHDV electrification architectures; 2. Identification and assessment of MHDV component technologies; 3. Assessment of potential performance and cost drivers; and, 4. Identification of R&D gaps where appropriate R&D and technology would accelerate commercial vehicle electrification. These gaps include shortfalls in technology, gaps in data, and inadequate knowledge and understanding. To inform this study, the National Renewable Energy Laboratory–Oak Ridge National Laboratory team examined the open literature; conducted workshops; assessed and analyzed data on more than 175 electrified MHDVs, including vehicle and powertrain specifications; and conducted one-on-one meetings with industry representatives. This systems-level information was concatenated and parsed with respect to key characteristics such as battery energy, power, and range compared across vehicle classes. Operational data of many conventional vehicles were also examined to help determine the requirements on electrified vehicle attributes such as range. Previous studies on the total cost of ownership of electrified MHDVs were reviewed to determine where improvements in technologies (efficiency, cost, maintenance) were needed to accelerate adoption. The team also assessed the state of the art in electric drivetrains for component specifications and challenges for the commercial vehicle sector, including electric machines, power electronics, and off-board high-power charging systems (extreme fast charging and beyond). The goal of this analysis was to identify the barriers to widespread adoption of commercial vehicle electrification technologies and prioritize the research and development gaps that need to be overcome to accelerate significant market penetration of these technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Assessment of Light-Duty Plug-in Electric Vehicles in the United States, 2010 – 2021

The number of plug-in electric vehicles (PEVs) sold in the United States has consistently grown since 2010, reaching 4% of the light-duty vehicle market in 2021. This report examines how the characteristics for these PEVs has changed over this decade, evaluating range, energy efficiency, costs, and performance. Given the vehicle characteristics, this report estimates miles driven, electricity consumption, petroleum reduction, and greenhouse gas emissions attributable to electric vehicles. This report also explores vehicle manufacturing and battery production, considering supply chains from battery cells to assembly. Over 2.1 million PEVs have been sold in the United States through December 2021, with 1.3 million of these all fully-electric battery electric vehicles (BEV), and 800,000 plug-in hybrid electric vehicles (PHEV) which have the capability of using gasoline. The sales-weighted average range for BEVs reached 290 miles in 2021 and 28 miles for PHEVs. We estimate that electric vehicles have driven 68 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.54% in 2021 and 2.5 billion gallons cumulatively through 2021. In 2021, PEVs used 6.1 terawatt-hours of electricity to drive 19.1 billion miles, offsetting 700 million gallons of gasoline. We find that this fuel switching reduced consumer fuel costs by $1.3 billion in 2021. Since 2010, 65% of PEVs sold in the United States have been assembled domestically, and over 110 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

25 ENERGY STORAGE↗
DOE OSTI2024

Adoption of Plug-in Electric Vehicles: Local Fuel Use and Greenhouse Gas Emissions Reductions Across the U.S.

The dependence on gasoline-powered light-duty automobiles has made U.S. households vulnerable to the burden of fuel costs. Tailpipe emissions from these vehicles constitute 58% of greenhouse gas (GHG) emissions in the U.S., which are damaging to the environment (EPA, 2023). The adoption of plug-in electric vehicles (PEVs) has been shown to effectively reduce fuel costs and GHG emissions. However, local effects on these benefits are not well understood by American consumers, potentially limiting adoption and therefore the realization of PEV benefits at scale (MacInnis & Krosnick, 2020; EY Americas, 2023). To fill this research gap, this study estimates the fuel cost savings and GHG emission reductions at the state and ZIP code levels by considering local fuel prices, vehicle class preference, average vehicle model year, fuel efficiencies, and driving intensities. The study's findings reveal that the adoption of PEVs can yield substantial benefits in terms of fuel cost savings and GHG emission reductions nationwide. Specifically, driving a battery electric vehicle (BEV) is estimated to result in annual savings of up to $\$2,200$, while driving a plug-in hybrid electric vehicle (PHEV) can lead to savings up to $\$1,500$, when compared to an internal combustion engine vehicle (ICEV) of equivalent size. Moreover, using population-weighted averages by ZIP code, BEVs and PHEVs show the potential to save 400 and 200 grams of carbon dioxide equivalent per mile, respectively, compared to a representative ICEV of the same class. The magnitude of fuel cost savings and emissions reduction vary by region due to various factors. Generally, regions with high gasoline prices, low electricity prices, preferences for larger vehicles, and high driving intensities tend to see relatively large fuel savings. The emissions reductions are more pronounced in areas with clean grids where consumer preferences lie with large vehicles. This regional variability underscores the importance of considering local contextual factors when assessing the potential benefits of PEV adoption. In more than 99% of U.S. ZIP codes, PEVs result in overall savings in fuel use (and subsequent costs) and GHG emissions. While not a central focus of this analysis, reductions in GHG tailpipe emissions from PEV adoption would also come with reductions in criteria pollutant emissions, contributing to improved local air quality depending on the PEV penetration, population density, and electricity generation infrastructure in the locality.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Method and apparatus for controlling a vehicle including an adaptive cruise control system

Operating a subject vehicle equipped with an adaptive cruise control system includes setting initial states for control parameters, including setting a desired vehicle speed and determining a desired following gap range, wherein the desired following gap range is associated with a lead vehicle. Operation is controlled via the adaptive cruise control system based upon the initial states for the control parameters. Operation also includes monitoring for presence of the lead vehicle. Upon detecting presence of the lead vehicle, an actual following gap is determined between the subject vehicle and the lead vehicle, and the initial states of the control parameters associated with the adaptive cruise control system are adjusted based upon the actual following gap between the subject vehicle and the lead vehicle, and the desired following gap range. Operation is controlled via the adaptive cruise control system based upon the adjusted initial states of the control parameters.

Zhao, Junfeng↗
DOE OSTI2024

System and method for vehicle coast control

A method for controlling vehicle propulsion includes identifying at least one route characteristic of a portion of a route being traversed by a vehicle. The method further includes determining a profile for a target vehicle speed based on the at least one route characteristic and a vehicle energy consumption profile. The method further includes selectively adjusting a vehicle speed control input based on the target vehicle speed profile. The method further includes communicating the vehicle speed control input to a vehicle propulsion controller to achieve the target vehicle speed profile.

Aggoune, Karim↗
DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. Here, the proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Spatially Resolved Domicile Charging Demands for Light-, Medium-, and Heavy-Duty Electric Vehicles in Virginia

The use of plug-in electric vehicles (PEVs) and resulting grid impacts are likely to grow rapidly, and evaluation of optimal smart charge management and grid integration strategies is warranted now. Evaluating distribution grid impacts requires fine-grained models of PEV operations to estimate charging loads across diverse vehicles at high spatial resolution. We propose such a model and consider a high-electrification scenario in Richmond and Newport News, Virginia. Our framework considers four categories of vehicle that are amenable to early aggressive electrification: light-duty passenger vehicles (LDV), trucks and vans with a focus on delivery or other local operations, school buses, and transit buses. These vehicles have a relatively consistent domicile, reducing the need for public charging infrastructure rollout to electrify. We apply a recent LDV model and propose new models for each vocation of medium- and heavy-duty vehicle, leveraging telematics data. We demonstrate our framework in Virginia and find energy demands in the region may total 15 GWh day, with most consumed by LDV. However, considering power demand at high spatial resolution reveals a different trend: LDVs have relatively small peak loads at specific sites (peak site demand around 800 kW) compared to average and high demand medium- and heavy-duty vehicle charging sites (peak site demand around 6,000 kW at a transit bus depot, 1,500 kW at a local freight hub, and 1,000 kW at a school). Our framework yields insights on the relative impacts of each vocation and enables future work to tailor grid integration strategies to each vehicle category.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Spatial and unobserved heterogeneity in consumer preferences for adoption of electric and hybrid vehicles: A Bayesian hierarchical modeling approach

The transition to low carbon vehicles known as alternative fuel vehicles (AFVs) is well underway. This transition has been motivated partly by consumer demand and partly by legislation such as the Zero Emission Vehicle mandate, which requires manufacturers to sell a certain percentage of their vehicles as AFVs. While the long-term adoption of AFVs (specifically, electric and hybrids) may take several years, there is a need to understand consumer preferences for AFV adoption and the pathways of AFV adoption from a national perspective. Therefore, this study sought to provide information about consumer preferences regarding AFV ownership while considering spatial and unobserved heterogeneity in consumer preferences, which can potentially impact societal transition to low carbon fueled vehicles. The 2017 National Household Travel Survey was used to calibrate Bayesian logit and hierarchical models. Here, the findings of these models reveal that higher gasoline prices contribute toward the adoption of battery electric vehicles. The results also reveal that the perceived disadvantages of AFVs for long commutes are the key barrier in wider adoption of AFVs. Interestingly, frequent use of the internet by consumers revealed a higher likelihood for purchase of hybrid vehicles. Furthermore, West Coast residents are observed to be a large portion of the early adopters and are more likely to purchase hybrids as compared to battery electric vehicles. The knowledge generated by this study has implications for making better informed decisions about AFV adoption and developing incentives to promote wider adoption of AFVs by overcoming their perceived disadvantages.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Regional Emissions Analysis of Light-Duty Battery Electric Vehicles

Light-duty battery electric vehicles (BEVs) can reduce both greenhouse gas (GHG) and criteria air pollutant (CAPs) emissions, when compared to gasoline vehicles. However, research has found that while today’s BEVs typically reduce GHGs, they can increase certain CAPs, though with significant regional variability based on the electric grid mix. In addition, the environmental performance of electric and gasoline vehicles is not static, as key factors driving emissions have undergone significant changes recently and are expected to continue to evolve. In this study, we perform a cradle-to-grave life cycle analysis using state-level generation mix and vehicle operation emission data. We generated state-level emission factors using a projection from 2020 to 2050 for three light-duty vehicle types. We found that BEVs currently provide GHG benefits in nearly every state, with the median state’s benefit being between approximately 50% to 60% lower than gasoline counterparts. However, gasoline vehicles currently have lower total NOx, urban NOx, total PM 2.5 , and urban PM 2.5 in 33%; 15%; 70%; and 10% of states, respectively. BEV emissions will decrease in 2050 due to a cleaner grid, but the relative benefits when compared to gasoline vehicles do not change significantly, as gasoline vehicles are also improving over this time.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

How to support EV adoption: Tradeoffs between charging infrastructure investments and vehicle subsidies in California

Supporting the adoption of zero-emission vehicle (ZEVs), including plug-in electric vehicles (EVs), has become a priority for governments due to their ability to reduce petroleum demand, improve air quality, and reduce carbon dioxide (CO2) emissions. Optimal strategies to accelerate EV adoption must weigh the relative value of alternative policy mechanisms to consumers, including public charging infrastructure and vehicle purchase subsidies. We use a historically validated light-duty vehicle consumer choice tool, the ADOPT model, to simulate personal light-duty vehicle adoption and related emissions in California. ADOPT is updated to incorporate a quantification of the tangible value of public charging infrastructure, allowing us to simulate the impact of investments in public charging infrastructure and vehicle purchase subsidies under different scenarios. We show that both policies result in increased EV adoption, with the most effective policy varying depending on vehicle technology assumptions. Under conservative technology improvement assumptions, infrastructure investments are most effective in promoting EV sales and reducing CO2 emissions, while under optimistic technology improvement assumptions a combination of infrastructure and subsidies best supports EV sales and CO2 emission reductions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Light-Duty Vehicle Choice Modeling and Benefits Analysis (van018)

The U.S. Department of Energy’s Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. The analysis in this poster is based on technical progress goals established in VTO and HFTO in the years immediately prior to and including 2020, and it summarizes the estimated energy and emissions benefits corresponding with achievement of those goals. The goals span research activities on batteries, electric drive technologies (EDT), combustion, lightweight materials, fuel cells, and hydrogen storage. The Automotive Deployment Options Projection Tool (ADOPT) is used to estimate the benefits for light-duty vehicles. ADOPT is a vehicle choice and stock model that estimates vehicle technology improvement impacts on sales, energy, and emissions. It includes all the existing vehicle options for realism, estimates their sales using extensively validated consumer preferences, creates new market-driven vehicle options through time, and rolls up sales to estimate energy and emissions. ADOPT takes in technology progress assumptions and applies these to the modeled vehicles through time. The assumptions are represented by a No Program scenario that reflects the technology improvements assumed to occur without further contributions from VTO or HFTO, and a Program Success scenario under which VTO and HFTO program goals are realized. The benefits are calculated by comparing ADOPT's estimated national-level energy and emissions resulting from the Program Success relative to the No Program scenario. By 2050, the Program Success scenario results in 11% less annual petroleum consumption and 10% less annual carbon emissions than the No Program scenario.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Empirical Evidence for the Potential Climate Benefits of Decarbonizing Light Vehicle Transport in the U.S. with Bioenergy from Purpose-Grown Biomass with and without BECCS

Climate mitigation scenarios limiting global temperature increases to 1.5 °C rely on decarbonizing vehicle transport with bioenergy production plus carbon capture and storage (BECCS), but climate impacts for producing different bioenergy feedstocks have not been directly compared experimentally or for ethanol vs electric light-duty vehicles. A field experiment at two Midwest U.S. sites on contrasting soils revealed that feedstock yields of seven potential bioenergy cropping systems varied substantially within sites but little between. Bioenergy produced per hectare reflected yields: miscanthus > poplar > switchgrass > native grasses ≈ maize stover (residue) > restored prairie ≈ early successional. Greenhouse gas emission intensities for ethanol vehicles ranged from 20 to -179 g CO 2 e MJ –1 : maize stover $\gg$ miscanthus ≈ switchgrass ≈ native grasses ≈ poplar > early successional ≥ restored prairie; direct climate benefits ranged from ~80% (stover) to 290% (restored prairie) reductions in CO 2 e compared to petroleum and were similar for electric vehicles. With carbon capture and storage (CCS), reductions in emission intensities ranged from 204% (stover) to 416% (restored prairie) for ethanol vehicles and from 329 to 558% for electric vehicles, declining 27 and 15%, respectively, once soil carbon equilibrates within several decades of establishment. Extrapolation based on expected U.S. transportation energy use suggests that, once CCS potential is maximized with CO 2 pipeline infrastructure, negative emissions from bioenergy with CCS for light-duty electric vehicles could capture >900 Tg CO 2 e year –1 in the U.S. In the future, as other renewable electricity sources become more important, electricity production from biomass would offset less fossil fuel electricity, and the advantage of electric over ethanol vehicles would decrease proportionately.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Powertrain distribution based on total cost of ownership for privately owned vehicles and TNC in the Chicago metropolitan area

From a Total Cost of Ownership (TCO) standpoint, the lowest-cost powertrain option depends on driving conditions and number of miles driven over the lifetime of the vehicle. Studies looking at the value proposition of powertrain options traditionally assess the energy consumption based on standardized drive cycles. TCO is then calculated using average yearly Vehicle Miles Travelled (VMT). This approach which looks at TCO at the aggregate level, does not take into consideration the variations in driving patterns and the spread of VMT that exist in the vehicle population. In this study, we first determine the driving pattern of each vehicle in the Chicago metropolitan area. We then consider different powertrain options for each vehicle, calculate its associated TCO and provide a powertrain distribution based on lowest TCO. Vehicles under consideration are Privately Owned Vehicles (POV) and vehicles used by Transportation Network Companies (TNC).

Freyermuth, Vincent↗
DOE OSTI2025

Incorporating Elevation in Traffic-Vehicle CO-Simulation: Issues, Impacts, and Solutions

Traffic-vehicle co-simulation couples microscopic traffic simulation with full-body vehicle dynamics to assess system-level impacts on mobility, energy, and safety with greater realism. Incorporating elevation is critical for accurately modeling vehicle behavior and energy use, especially for gradient-sensitive vehicles such as electric and heavy-duty trucks. However, raw elevation data often contain noise, discontinuities, and inconsistencies. While such issues may be negligible in traditional traffic simulations, they significantly affect traffic-vehicle co-simulations where vehicle dynamics are sensitive to road grade variations. This paper investigates the impact of unprocessed elevation data on vehicle behavior and energy consumption using a 42-mile simulation along Interstate 81. We propose an elevation processing workflow that can mitigate the effects stem from elevation data issues, improving the realism and stability of traffic-vehicle co-simulation. Results show that the method effectively removes noise and abrupt elevation transitions while preserving roadway geometry.

Xu, Guanhao [ORNL] (ORCID:0000000214326357)↗
DOE OSTI2022

Vehicle Residual Value Analysis by Powertrain Type and Impacts on Total Cost of Ownership

Vehicle depreciation is a key factor in determining the total cost of vehicle ownership and consumer purchase behavior. This report examines how light-duty-vehicle residual values have evolved over time for conventional and advanced vehicle technologies, accounting for important factors such as market segment, size class, and country of assembly. Advancements in electric vehicle technology have led to plug-in vehicles exhibiting depreciation curves similar to those of conventional vehicles. This report compares two methods for determining depreciation trends (snapshot method and time-series method) in order to identify potential impacts on calculating vehicle total cost of ownership given differing data availability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗