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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Approximate Dynamic Programming With Enhanced Off-Policy Learning for Coordinating Distributed Energy Resources

Herein this paper proposes an innovative approximate dynamic programming (ADP) method for distributed energy resource coordination with the loss of life of battery energy storage system (BESS) explicitly modeled. The dispatch policy is designed to account for both calendrical and cyclical aging effects on BESS, explicitly modeling the impacts of ambient temperature on BESS lifespan. The proposed ADP employs an adaptive critic method and enhanced off-policy deterministic policy gradient (DPG) strategy, addressing the limitations of the on-policy gradient-based ADP approaches, including inadequate exploration, low data usage, and computational complexity. In particular, a customized policy is proposed to guide the algorithm to explore some promising decisions and thereby improve exploration capability and learning efficiency compared to conventional DPG-based learning approaches, which may struggle to find a global optimum due to random noisy action-based exploration or require expert demonstration with extra effort. The proposed method is illustrated using the IEEE 123-node system and compared with the existing ADP methods to prove solution accuracy and demonstrate the effects of incorporating degradation models into control design. Case studies showed that the proposed ADP effectively coordinates DERs with a 10 times smaller optimization gap compared to existing methods, and the incorporation of the BESS life loss model ensures the expected lifespan and results in significant cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Structural dependence of crystallization in glasses along the nepheline (NaAlSiO 4 ) ‐ eucryptite (LiAlSiO 4 ) join

Abstract Lithium and sodium aluminosilicates are important glass‐forming systems for commercial glass‐ceramics, as well as being important model systems for ion transport in battery studies. In addition, uncontrolled crystallization of LiAlSiO 4 (eucryptite) in high‐Li 2 O compositions, analogous to the more well‐known problem of NaAlSiO 4 (nepheline) crystallization, can cause concerns for long‐term chemical durability in nuclear waste glasses. To study the relationships between glass structure and crystallization, nine glasses were synthesized in the Li x Na 1‐ x AlSiO 4 series, from x = 0 to x = 1. Raman spectra, nuclear magnetic resonance ( NMR ) spectroscopy (Li‐7, Na‐23, Al‐27, Si‐29), and X‐ray diffraction were used to study the quenched and heat‐treated glasses. It was found that different LiAlSiO 4 and NaAlSiO 4 crystal phases crystallize from the glass depending on the Li/Na ratio. Raman and NMR spectra of quenched glasses suggest similar structures regardless of alkali substitution. Li‐7 and Na‐23 NMR spectra of the glass‐ceramics near the endmember compositions show evidence of several differentiable sites distinct from known Li x Na 1‐ x AlSiO 4 crystalline phases, suggesting that these measurements can reveal subtle chemical environment differences in mixed‐alkali systems, similar to what has been observed for zeolites.

Marcial, José↗
DOE OSTI2014

Project Startup: Evaluating the Performance of Frito Lay's Electric Delivery Trucks (Fact Sheet)

The Fleet Test and Evaluation Team at the National Renewable Energy Laboratory (NREL) is evaluating the in-service performance of 10 medium-duty Smith Newton electric vehicles (EVs) and 10 comparable conventional diesel vehicles operated by Frito Lay North America in the Seattle, Washington, area. Launched in late 2013, the on-road portion of this 12-month evaluation focuses on collecting and analyzing vehicle performance data, such as fuel economy and maintenance costs, to better understand how to optimize the use of such vehicles in a large-scale commercial operation. In addition to the on-road portion of this evaluation, NREL is analyzing charging data to support total cost of ownership estimations and investigations into smart charging opportunities. NREL is also performing a battery life degradation analysis to quantify battery pack health, track battery performance over time, and determine how various drive cycles and battery charging protocols impact battery life.

25 ENERGY STORAGE↗
DOE OSTI2023

A Power Takeoff Device for a Small Marine Hydrokinetic Turbine Deployed from an Unmanned Floating Platform

The design, development, and field-testing of a power takeoff (PTO) device equipped with a ball-type continuously-variable transmission (B-CVT) for a small marine hydrokinetic (MHK) turbine deployed from a floating unmanned autonomous mobile catamaran platform is described. The turbine is a partially-submerged multi-blade undershot waterwheel (USWW). The objective is to develop a PTO for the optimal conversion of the MHK energy harnessed by the turbine to electric power, which is stored in battery banks onboard the MHK platform. Modeling, simulation, and bench testing of the USWW and PTO show the feasibility of utilizing the B-CVT's variable gear ratio to decouple the USWW and generator speeds, maintaining the waterwheel within its optimal tip speed ratio (TSR) while varying the generator speed, thereby increasing the efficiency of the PTO. Results of bench and field testing in support of characterizing the power conversion capabilities of the PTO are described. The system being developed is in support of potential self-powered autonomous mobile recharge stations for unmanned aerial vehicles (U A V s) operating in coastal zones. Field tests of the complete MHK platform with 9 blades on the waterwheel and wheel sub mergences of 10 and 12 inches (full blade submergence) were performed. The overall proof-of-concept was successfully demonstrated, with the system satisfactorily capturing and converting water flow energy into electricity. The feasibility of utilizing the B-CVT as a means of increasing the PTO power capture capabilities and efficiency is analyzed.

Pimentel, Hugo↗
NASA NTRS1986

Multikilowatt Bipolar Nickel/Hydrogen Battery

High energy densities appear feasible. Nickel/hydrogen battery utilizing bipolar construction in common pressure vessel, addressing needs for multikilowatt storage for low-Earth-orbit applications, designed and 10-cell prototype model tested. Modular-concept-design 35-kW battery projected energy densities of 20 to 24 Wh/b (160 to 190 kj/kg) and 700 to 900 Wh/ft3 (90 to 110 MJ/m3) and incorporated significant improvements over state-of-the-art storage systems.

Source record↗
NASA NTRS2000

Thermal Aspects of Lithium-Ion Cells

Objective of this investigation is to provide the necessary inputs for a thermal model of the Li-ion battery for the Mars 2001 Lander. Two alternate configurations of this battery are under development: a) prismatic parallel plate, and b) cylindrical spiral wound. Required thermal inputs for both consist of the following: a) heat generation rates, b) thermal mass, and c) thermal conductivity. Thermal mass and conductivity were computed on the basis of known properties and configuration of the cell components. The heat generation rates were taken as the product of current and difference between open circuit voltage (OCV) and operating voltages (CCV) at a given state-of charge (SOC). Herein, it was assumed that the enthalpy voltage was equal to the OCV. OCV vs SOC data were obtained experimentally and CCV vs SOC were taken from previously obtained discharge data.

Frank, H.↗
NASA NTRS2008

Power System Trade Studies for the Lunar Surface Access Module

A Lunar Lander Preparatory Study (LLPS) was undertaken for NASA's Lunar Lander Pre-Project in 2006 to explore a wide breadth of conceptual lunar lander designs. Civil servant teams from nearly every NASA center responded with dozens of innovative designs that addressed one or more specific lander technical challenges. Although none of the conceptual lander designs sought to solve every technical design issue, each added significantly to the technical database available to the Lunar Lander Project Office as it began operations in 2007. As part of the LLPS, a first order analysis was performed to identify candidate power systems for the ascent and descent stages of the Lunar Surface Access Module (LSAM). A power profile by mission phase was established based on LSAM subsystem power requirements. Using this power profile, battery and fuel cell systems were modeled to determine overall mass and volume. Fuel cell systems were chosen for both the descent and ascent stages due to their low mass. While fuel cells looked promising based on these initial results, several areas have been identified for further investigation in subsequent studies, including the identification and incorporation of peak power requirements into the analysis, refinement of the fuel cell models to improve fidelity and incorporate ongoing technology developments, and broadening the study to include solar power.

Kohout, Lisa, L.↗
NASA NTRS2013

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

Sankararaman, Shankar↗
NASA NTRSDate not supplied

Hybrid Modeling for Complex Systems Health Management

The research work presents application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. The powertrain model consists of Li-ion batteries, electronic speed controller with pulse-width modulation, and brush-less DC motor with connected propeller. Results obtained from combination of laboratory and simulation tests are discussed in this work.

PINNS↗
DOE OSTI2010

The added economic and environmental value of plug-in electric vehicles connected to commercial building microgrids

Connection of electric storage technologies to smartgrids or microgrids will have substantial implications for building energy systems. In addition to potentially supplying ancillary services directly to the traditional centralized grid (or macrogrid), local storage will enable demand response. As an economically attractive option, mobile storage devices such as plug-in electric vehicles (EVs) are in direct competition with conventional stationary sources and storage at the building. In general, it is assumed that they can improve the financial as well as environmental attractiveness of renewable and fossil based on-site generation (e.g. PV, fuel cells, or microturbines operating with or without combined heat and power). Also, mobile storage can directly contribute to tariff driven demand response in commercial buildings. In order to examine the impact of mobile storage on building energy costs and carbon dioxide (CO2) emissions, a microgrid/distributed-energy-resources (DER) adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs applying CO2 taxes/CO2 pricing schemes. The problem is solved for a representative office building in the San Francisco Bay Area in 2020. By using employees' EVs for energy management, the office building can arbitrage its costs. But since the car battery lifetime is reduced, a business model that also reimburses car owners for the degradation will be required. In general, the link between a microgrid and an electric vehicle can create a win-win situation, wherein the microgrid can reduce utility costs by load shifting while the electric vehicle owner receives revenue that partially offsets his/her expensive mobile storage investment. For the California office building with EVs connected under a business model that distributes benefits, it is found that the economic impact is very limited relative to the costs of mobile storage for the site analyzed, i.e. cost reductions from electric vehicle connections are modest. Nonetheless, this example shows that some economic benefit is created because of avoided demand charges and on-peak energy. The strategy adopted by the office building is to avoid these high on-peak costs by using energy from the mobile storage in the business hours. CO2 emission reduction strategy results indicate that EVs' contribution at the selected office building are minor.

25 ENERGY STORAGE↗
DOE OSTI2023

MiGRIDS

MiGRIDS is a software that models islanded microgrid power systems with different controls and components. For example, using load and resource data from a microgrid, you could model it with additional wind turbines, battery etc. You could also try out different dispatch schemes to see which one worked best. MiGRIDS is designed to help optimize the size and dispatch of grid components in a microgrid. While a grid connect feature is expected to be added in the future, islanded operation is the focus. Note that this is a basic implementation and more features and functionality (such as a GUI) are coming! MiGRIDS runs time-step energy balance simulations for different grid components and controls. In smaller microgrid environments, dispatch decisions are being made on the order of seconds. In order to fully capture their effect, this tool lets you run simulations on the order of seconds. The end result is a more realistic representation of what can be achieved by integrating different components and control strategies in a grid.

Morgan, Tawna↗
DOE OSTI2024

Long duration battery sizing, siting, and operation under wildfire risk using progressive hedging

Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. Here we formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 min of wall-clock time.

25 ENERGY STORAGE↗
DOE OSTI2024

Battery-powered bargains? Assessing electric vehicle resale value in the United States

Abstract The resale market will play a critical role in expanding plug-in electric vehicle (PEV) adoption to middle- and lower-income households. Understanding PEV depreciation trends in comparison to those of conventional gasoline vehicles (CVs) is critical for assessing PEV affordability and informing relevant policy, such as subsidies for used PEVs. We deliver comprehensive, high-resolution estimates of value retention rates at the make-model level in the United States for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and CVs using a database of 9 million used cars listed online between 2016 and 2022. While BEVs and PHEVs have depreciated at faster rates than CVs, this trend is changing, with newer model year BEVs and those with larger ranges have significantly higher retention rates than older model years with smaller ranges. Tesla BEVs are a notable exception following the opposite trend, with earlier model years holding their value better than newer model years. Subsidized BEVs in the new market are associated with lower prices for the same model in the resale market, with the $7500 federal subsidy translating to a 3% lower resale price on average. Finally, disruptions from the COVID-19 pandemic have affected affordability across all vehicles, with mean listing prices rising 37% and 39% for CVs and BEVs, respectively, from January 2020 to March 2022 in inflation-adjusted 2019 dollars.

Environmental Sciences & Ecology↗