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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Electrifying Transit: A Guidebook for Implementing Battery Electric Buses

This guidebook is organized to give transit decision-makers and relevant stakeholders an overview of BEB facts, data, and considerations important for planning their implementation in a variety of jurisdictions. First, the benefits and barriers for BEB are identified. Second, BEB basics in terms of the major components, including a) the bus, b) the battery, and c) the numerous charging options. BEB introduces new, high demand loads as the buses are charging and thus have a number of interactions with the electricity grid and the utility, which are explored third. Operation and maintenance of BEBs are considered fourth as BEBs should not be operated and maintained in the same approach as diesel buses. Fifth, the costs of BEB prices are summarized – and the choices of bus and battery are explored in how they impact BEB prices. Funding and financing options that support BEBs and their charging stations are also explored. Safety is a key consideration for BEBs and the guidebook touches upon codes and standards, hazards, and emergencies. The final section examines project execution, bringing together information from all the other sections so that long-term planning, route analysis, and fleet and infrastructure planning can be considered in the preparation of BEB deployment, and then deployment can be evaluated on a regular basis. A final conclusion revisits the information covered in the guidebook.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Financial Analysis of Battery Electric Transit Buses

A baseline bus fleet and battery electric bus investment scenario was developed based on the average or common parameters of existing battery electric bus (BEB) fleets. A discounted cashflow analysis was done that found the baseline fleet to have a net present value of $785,000 and simple payback of 3.3 years. The 33 main parameters were then swung ±50% to determine their relative influence on NPV and were ranked accordingly. Then parameter volatility was estimated by dividing the range of observed values by the baseline value. The parameters that are most influential and volatile were highlighted as the ones fleet managers should focus on when determining if BEBs are a good investment option for them. These top parameters are 1) BEB purchase price, 2) purchase price of foregone diesel bus, 3) grant amount, 4) maintenance costs of foregone diesel bus, 5) annual vehicle miles traveled.

25 ENERGY STORAGE↗
DOE OSTI2023

Analysis of a Runtime Data Sharing Architecture over LTE for a Heterogeneous CAV Fleet

This paper describes a lightweight runtime architecture for telemetry, communication, and control of cars deployed with advanced driver assistance systems where a human is in the loop with the car, via an LTE connection. The system architecture supports both local control decisions based on car sensors and safety algorithms as well as high-level input from external systems that may provide insight into traffic state ahead of sensor data. Implementation of the architecture is done in ROS and depends on open-source software packages for runtime decoding of information from the vehicle’s controller area network (CAN) and integration of GPS data from accompanying sensors. The contribution of the paper is to describe the overall architecture, the data it can communicate to other systems, performance of the system at runtime, and challenges faced when deploying the architecture across a heterogeneous fleet. Preliminary results from analysis of test data will provide insights into whether the use of high-latency communication can be effective for societal-scale intelligent transportation systems when applied in future scenarios

Richardson, Alex↗
DOE OSTI2024

Automobile and Technology Lifecycle-Based Assignment (ATLAS) v2.0.12

ATLAS is a comprehensive vehicle transaction and technology adoption microsimulator. ATLAS evolves the fleet mix of individual households by simulating the transaction (vehicle addition, disposal, and replacement) and choice (vehicle type, vintage, powertrain, and tenure) decisions in response to the co-evolving demographics, land use, and vehicle technology simulations. Different from the existing vehicle models that are either static or aggregated (e.g. stock model), ATLAS is fully disaggregated and dynamic following a sequential and circumstantial decision-making trajectory. This fine-grained approach not only enhances the realism of the simulation but also provides a nuanced understanding of the dynamics inherent in vehicle fleet evolution. ATLAS outputs are fully compatible with subsequent agent-based transportation modeling system and can enable distributional effect analysis regarding the fleet turnover among heterogeneous populations. ATLAS expands the typical new sale focused vehicle choice modeling to including used vehicle transactions that are of increasing interests to understanding the vehicle adoption behavior among lower income households.

Jin, Ling↗
DOE OSTI2024

Estimating Electrification Potential for Class 8 Regional-Haul Trucks

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. As part of the North American Council for Freight Efficiency's (NACFE's) Run on Less Depot data workshop, NREL sought to understand how Tesla semi-trucks would perform in real-world regional haul applications. Analysis reveals that the modeled Tesla trucks, with an average efficiency of 1.78 kWh/mi, struggle to achieve full operational coverage using current battery and charging configurations assuming operations remain unchanged. However, in an extreme case where ubiquitous charging exists, 100% EV coverage is possible for the given drive cycles. These findings highlight the trade-off between battery size and charge rate in electrification potential and emphasize the necessity for advancements in charging infrastructure to enable electric trucks for regional haul operations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Well‐to‐wheels analysis of greenhouse gas emissions for passenger vehicles in Middle East and North Africa

Battery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO 2 eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

PV Fleet Performance Data Initiative: Performance Index-Based Analysis

In this analysis, we report on 250 PV systems throughout the US, comprising 157 MWdc of system capacity and over 10,000 monthly PI values based on high-frequency (subhourly) energy data and satellite-based resource data. The distribution of PI values is analyzed, and multiple causes of underperformance are assessed, including first-year startup issues, snowfall and inverter downtime. An initial distribution of raw monthly PI values was collected with mean measured / modeled performance of PI = 0.935. After correcting for the three identifiable loss factors mentioned above, an average monthly performance of PI = 0.994 was obtained, with a distribution closely following a Gumbel Extreme Value distribution. In particular, inverter availability was found to contribute a system energy loss of 2.3% on average, except in the first six months of operation when availability losses are closer to 8%. Other startup issues beyond inverter downtime such as partial string outage contributed an additional 5% underperformance in the first year of operation across the fleet. Winter performance was also found to be 5%-10% below summer performance on average, likely due to snowfall. A simple linear relationship was found between snow loss and monthly snowfall accumulation in cm, indicating between 6% - 40% loss in months with nonzero snowfall, depending on climate.

14 SOLAR ENERGY↗
DOE OSTI2024

Electrification Analysis: All Aboard America!

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, an electrification analysis for the Bustang motorcoach fleet operated by All Aboard America! Holdings Inc. (AAA). NREL installed logging devices and collected operational data on nine 40-foot Bustang motorcoaches operating on fixed routes from May 2022 through August 2022. The analysis determined that partial fleet electrification may be feasible with electrified motorcoach options currently on the market. While this fleet faces significant challenges to electrification given current market options due to demanding range requirements and relatively limited charging opportunities, vehicles operating on the shorter, lower-grade routes along the I-25 corridor show more immediately available electrification potential. Increases in available battery capacity and the availability of fast-charging locations along I-70 routes are likely critical for electrification of the full fleet.

AAA↗
DOE OSTI2020

Feasibility of Operating a Heavy-Duty Battery Electric Truck Fleet for Drayage Applications

Vehicle fleet electrification is regarded as one major pathway toward achieving energy independence and reducing air pollution and greenhouse gas emissions. Compared to light-duty and medium-duty vehicles, electrification of heavy-duty vehicles, especially Class 8 trucks, is more challenging owing to the battery size required to attain the driving range necessary for their operating goals. As drayage trucks generally have a limited daily mileage, return to a home base every night, and spend a large amount of time creeping and idling, drayage operation has been the first targeted application for Class 8 electric trucks. The feasibility of operating battery electric drayage trucks at the individual vehicle level has recently been demonstrated. However, questions remain as to whether these trucks are capable of meeting the needs of typical drayage operation at the fleet level. Here we present a feasibility analysis of operating an electric truck fleet based on real-world operation data of a diesel drayage operator in Southern California. Second-by-second activity data collected from 20 trucks in the fleet were used to estimate the corresponding electric energy consumption and the state of charge of the battery using a microscopic electric energy consumption model. An algorithm for generating tours of drayage activity from the collected data was developed and implemented. Multiple scenarios with different battery charging and truck scheduling assumptions were analyzed. The results show that 85% of the tours could be served by electric trucks if there is opportunity for charging at the home base during the time gap between consecutive tours.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electrification Analysis: Manhattan Beer

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, the Manhattan Beer Electrification Project. This project determined that Class-8 beverage distribution trucks operating in Manhattan show substantial electrification potential due to daily driving distances below 50 miles and low average speeds of 22mph or less. Their duty cycle needs can often be met by even modestly sized batteries and charging infrastructure. Vulnerable communities near their routes would benefit from fleet electrification.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗
DOE OSTI2026

Understanding Advanced Vehicle Technology Adoption Potential in Commercial Fleets Across Major Trucking Sectors

Adopting advanced vehicle technologies, such as battery-electric, hybrid, and hydrogen fuel cell vehicles, can be an effective strategy for reducing fleet owners' operating costs. However, different trucking sectors, such as private and for-hire carriers and short- or long-haul operations, may face unique challenges in adopting those vehicle technologies due to their own operational needs and budget constraints. Current studies on fleet-wide vehicle technology projections frequently overlook such sectoral differences and fail to capture variation in adoption potential across sectors. This study addresses this gap by analyzing the disparities in the total cost of ownership (TCO) and payback period (PBP) among a large and heterogeneous sample of fleet owners. It aims to understand the sectoral differences in the long-term potential for adopting advanced vehicle technologies. Utilizing the 2021 US Vehicle Inventory and Use Survey (US VIUS), which offers data on various commercial vehicle sectors, their operational patterns, and current vehicle assets, this research estimates the TCO and PBP for individual trucks over multiple future years. The results reveal variation in the cost-effectiveness of different vehicle technologies across trucking sectors, as well as the potential technology landscape in both the short and long term. The findings from this study can inform policymakers and practitioners on how to prioritize sectors with lower barriers for advanced vehicle technology adoption and support industries that face challenges in switching to advanced vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Effect of adaptive cruise control on fuel consumption in real-world driving conditions

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

EV Grid Integration: Pass Cars and Commercial Fleets

The presentation discusses grid integration of EVs, both light duty vehicles and commercial fleets under these topics - Grid impact analysis for pass cars, Opportunities for smart charging for pass cars, Considerations for commercial fleet electrification, and DER Co-optimization for improved business case.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Estimating the Breakeven Cost of Delivered Electricity to Charge Class 8 Electric Tractors

As vehicle electrification expands from the light-duty sector to include larger commercial medium- and heavy-duty vehicles, businesses need to decide if electrification is appropriate for their fleets. A key factor in this decision will be the total cost of ownership for on-road electric Class 8 tractors compared to their combustion counterparts. This report examines the breakeven price of electricity for electric Class 8 tractor charging to address its importance in the total cost of ownership for the operation of an electrified fleet and to account for the inherent differences of higher-power charging. To understand the likely cost of delivered energy--in this report the breakeven price--to charge electric tractors, this study followed an analysis framework that considers a wide range of factors to estimate the breakeven price to charge at various station types. This requires an estimation of electric vehicle adoption trajectories and analysis of real-world fleet data to assess energy needs of heavy-duty electric tractors and to determine expected charging station demand over time. Station demand informs the level of electric vehicle supply equipment (EVSE) deployment that is necessary at each station type, as well as the site utilization and anticipated load profiles. Then, by accounting for a wide range of capital investments, operating costs, and other expenses, a breakeven price of energy is determined.

33 ADVANCED PROPULSION SYSTEMS↗