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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Utility-Scale Lithium-Ion Storage Cost Projections for Use in Capacity Expansion Models

This work presents U.S. utility-scale battery storage cost projections for use in capacity expansion models. We create battery cost projections based on a survey of literature cost projections of battery packs and balance of system costs, with a focus on lithium-ion batteries. Low, mid, and high cost trajectories are created for the overnight capital costs and the operating and maintenance costs. We then demonstrate the impact of these cost projections in the Regional Energy Deployment System (ReEDS) capacity expansion model. We find that under reference scenario conditions, lower battery costs can lead to increased penetration of variable renewable energy, with solar photovoltaics (PV) seeing the largest increase. We also find that additional storage can reduce renewable energy curtailment, although that comes at the expense of additional storage losses.

25 ENERGY STORAGE↗
NASA NTRS2024

Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations

This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.

Assurance↗
DOE OSTI2023

Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.

25 ENERGY STORAGE↗
DOE OSTI2018

Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm

In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. Additionally, this would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm’s known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. Finally, as shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system.

17 WIND ENERGY↗
NASA NTRS1976

Nickel cadmium cell composite reliability model

Ni-Cd battery reliability has become a critical design parameter with the increasing need for longer life and optimally designed secondary battery power systems in space as well as in more mundane applications. It is the purpose of this paper to present a state-of-the-art Ni-Cd cell reliability model as a function of cumulative cyclic operation, cell temperature and cyclic depth-of-discharge. In addition, a simple and convenient cell failure analysis technique will be presented in which the reliability, the distribution, and the distribution parameters can be determined by visual inspection.

Kirsch, W. W.↗
DOE OSTI2019

Modeling framework for multiphysics-multiscale behavior of Si–C composite anode

To achieve the urgent requirement for high energy density in lithium-ion batteries, Si-C composite anode has been spotlighted as one of the most promising alternatives for next-generation batteries. Additionally, a comprehensive computational model is indispensable in the developing process of the excellent performance of anode material due to the low-realizability, inconvenience, and high-cost of experiments. Herein, a simultaneous multiphysics-multiscale model aiming at the Si-C composite anode was developed. This model was then used to study the effects of Si percentage, mechanical constraint and charging rate in terms of electrochemical and mechanical performances. A better design to achieve high capacity was proposed based on the computational results. Results provide a powerful tool in the design, development, and evaluation of high energy density lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2019

Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios

Energy storage system plays an important role in modern power systems for mitigating the variation and intermittency of renewable energy sources. The Lithium-ion battery is currently the most widely used solution for energy storage system. However, its high cost is considered as one of the major barriers hampering the integration of renewable energy and the adoption of electric vehicles. Reusing electric vehicle batteries seems a promising solution to the aforementioned problem. Based on a dynamic degradation model of Lithium-ion batteries, this paper first compares the profits that second-life and fresh batteries can bring to the wind farm. Model predictive control is adopted to solve an hourly optimal wind scheduling problem and maximize the profit of wind farm owner. Here, the optimal size of battery is determined and then the comparison of second-life and fresh batteries is conducted taking the battery degradation, the profit of the wind farm owner, and various remaining capacities of battery into account. Two case studies in USA and Denmark are conducted and the analysis shows that given the current prices of wind energy and Lithium-ion batteries, reusing batteries is not worthwhile for the studied wind farms, but it may outperform fresh batteries in the future if the wind energy price decreases much faster than the battery price.

17 WIND ENERGY↗
DOE OSTI2021

Integrated Multiscale Modeling for Design of Robust 3D Solid-State Lithium Batteries - FY21 Annual Report

This project is developing a multiscale, multi-physics modeling framework for probing the effects of materials microstructure and device architecture on ion transport within 3D ceramic solid-state battery materials, with the goal of enhancing performance and reliability. The project has three primary objectives: (1) integrate multi physics and multiscale model components; (2) understand interface- and microstructure-derived limitations on ion transport; and (3) derive key structure-performance relations for enabling future optimization.

25 ENERGY STORAGE↗
DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Critically assessing sodium-ion technology roadmaps and scenarios for techno-economic competitiveness against lithium-ion batteries

Sodium-ion batteries have garnered notable attention as a potentially low-cost alternative to lithium-ion batteries, which have experienced supply shortages and price volatility for key minerals. Here we assess their techno-economic competitiveness against incumbent lithium-ion batteries using a modelling framework incorporating componential learning curves constrained by minerals prices and engineering design floors. We compare projected sodium-ion and lithium-ion price trends across over 6,000 scenarios while varying Na-ion technology development roadmaps, supply chain scenarios, market penetration and learning rates. Assuming that substantial progress can be made along technology roadmaps via targeted research and development, we identify several sodium-ion pathways that might reach cost-competitiveness with low-cost lithium-ion variants in the 2030s. In addition, we show that timelines are highly sensitive to movements in critical minerals supply chains—namely that of lithium, graphite and nickel. Our modelled outcomes suggest that being price advantageous against low-cost lithium-ion variants in the near term is challenging and increasing sodium-ion energy densities to decrease materials intensity is among the most impactful ways to improve competitiveness.

25 ENERGY STORAGE↗
DOE OSTI2019

Phase-field model for diffusion-induced grain boundary migration: An application to battery electrodes

Diffusion-induced grain boundary migration is a phenomenon in which a grain boundary moves in response to the driving forces generated by diffusing solute species. For example, diffusing solute species change the atomic volume in the host material, either by filling a vacancy with a misfitting solute atom or by expanding host lattices through interstitial diffusion. These volume changes are inhomogeneous and are stored as elastic energy in the material that drives grain boundaries. In this paper, we introduce our previously developed Cahn-Hilliard–phase-field-crystal model (CH-PFC) as a computational tool to investigate diffusion-induced grain boundary migration in crystalline materials. This multiscale phase-field model couples the composition field of a diffusing species with the crystallographic texture of a host material. We apply the CH-PFC model to battery electrodes and investigate whether interstitial solute diffusion induces grain growth in FePO 4 /LiFePO 4 electrodes. To this end, we compute grain growth in 60 FePO 4 electrodes by conducting two parallel trials: In the first trial, we cycle the electrode and calculate diffusion-induced grain growth. In the second trial, we do not cycle the electrode and calculate curvature-driven grain growth. We find a statistically significant grain growth in the cycled electrodes and negligible grain growth in the noncycled electrodes. Altogether, we show that the CH-PFC model not only predicts electrode microstructures as a function of the Li composition, but also predicts the crystallographic features of an electrode during battery operation.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Modeling flow distribution and pressure drop in redox flow batteries

The combination of interdigitated flow fields (IDFF) with porous electrodes offers lower pressure drop and better performance than conventional flow‐through porous electrodes in redox flow batteries. Comprehensive three‐dimensional and simplified one‐dimensional + two‐dimensional models describing flow uniformity and pressure losses within flow through, parallel, and interdigitated flow fields were developed and used to demonstrate the benefits of IDFF. Analytical solutions for IDFF that compare favorably with computational fluid dynamics quantify the trade between pressure loss and velocity maldistribution both along the channels and within the electrode. © 2018 American Institute of Chemical Engineers AIChE J , 64: 3746–3755, 2018

MacDonald, Malcolm↗
DOE OSTI2023

Chapter 6: Abuse Response of Batteries Subjected to Mechanical Impact

Electrochemical and thermal models to simulate nominal performance and abuse response of lithium-ion cells and batteries have been reported widely in the literature. Studies on mechanical failure of cell components and how such events interact with the electrochemical and thermal response are relatively less common. This chapter outlines a framework developed under the Computer Aided Engineering for Batteries program to couple failure modes resulting from external mechanical loading to the onset and propagation of electrochemical and thermal events that follow. Starting with a scalable approach to implement failure criteria based on thermal, mechanical, and electrochemical thresholds, we highlight the practical importance of these models using case studies at the cell and module level. The chapter also highlights a few gaps in our understanding of the comprehensive response of batteries subjected to mechanical crash events, the stochastic nature of some of these failure events, and our approach to build safety maps that help improve robustness of battery design by capturing the sensitivity of some key design parameters to heat generation rates under different mitigation strategies.

abuse simulations↗
DOE OSTI2024

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. Here, we present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.

25 ENERGY STORAGE↗
DOE OSTI2016

Understanding Performance Limitations to Enable High Performance Magnesium-Ion Batteries

We developed a mathematical model in order to investigate the performance limiting factors of Mg-ion battery with a Chevrel phase (Mg x Mo 6 S 8 ) cathode and a Mg metal anode. Furthermore, the model was validated using experimental data from the literature [Cheng et al., Chem. Mater., 26, 4904 (2014)]. Two electrochemical reactions of the Chevrel phase with significantly different kinetics and solid diffusion were included in the porous electrode model, which captured the physics sufficiently well to generate charge curves of five rates (0.1C–2C) for two different particle sizes. Limitation analysis indicated that the solid diffusion and kinetics in the higher-voltage plateau limit the capacity and increase the overpotential in the Cheng et al.’s thin (20-μm) electrodes. The model reveals that the performance of the cells with reasonable thickness would also be subject to electrolyte-phase limitations. Finally, the simulation also suggested that the polarization losses on discharge will be lower than that on charge, because of the differences in the kinetics and solid diffusion between the two reactions of the Chevrel phase.

25 ENERGY STORAGE↗
DOE OSTI2022

Influence of Hybridization on the Capacity Value of PV and Battery Resources

Utility-scale systems that combine solar photovoltaic and battery (PV+battery) technologies are growing in popularity on the U.S. bulk power system. The business case for PV+battery systems depends on both their ability to reduce costs and their ability to generate value synergies associated with the provision of energy, capacity, and ancillary services. Capacity value can constitute a significant portion of the value PV+battery hybrids provide to the grid (e.g., through avoided or deferred capacity) and receive through revenues. Throughout this report, we define capacity value as the monetary value of a plant's contribution towards the planning reserve margin, which ultimately depends on market rules and structures. PV+battery hybrids do not always fit into current market structures because of the interactions between the PV and battery components. Unique considerations for the capacity value of PV+battery hybrids include the disparate nature of participation models for PV and battery technologies in existing market rules and the potential influence of a shared interconnection capacity; limitations imposed by a shared inverter; limited ability to charge the battery in advance of capacity events if charging must be sourced from the coupled PV; and challenges or uncertainties associated with co-optimizing the operations of the PV and battery components. Grid operators are currently considering how market structures can be modified to optimally determine the capacity value provided by PV+battery systems, and the rules of how they are integrated into markets are still being written. As with any resource, poorly designed rules could increase the cost of energy and reduce system reliability, while well-designed rules could allow markets to receive the full benefits hybrid systems can offer without overcompensating them for the services they provide. Well-designed rules for PV+battery systems must consider the unique aspects listed above, while leveraging the commonalities with existing resource types. In this report, we summarize the technical capability and market rules that influence the capacity value of PV+battery systems. We further discuss the potential tradeoffs between computational complexity and accuracy for the various ways in which grid operators can credit PV+battery systems for capacity. Finally, we describe markets for capacity, survey current wholesale market rules applying to PV+battery systems, and provide a snapshot of the current regulatory landscape for PV+battery systems.

14 SOLAR ENERGY↗