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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Process design for calcination of nickel-based cathode materials by in situ characterization and multiscale modeling

Developing battery materials towards commercial use, from the early discovery through synthesis, processing, scaling up, and eventually to industrial production, may take decades. A notable example is Ni-based layered oxides, which were discovered as early as 1950s and intensively pursued as cathode active materials (CAMs) since the early 90s but have yet to realize their full commercial potential. Significant efforts have been devoted to materials development aiming at improving performance, far less to process development for large-scale synthesis and processing of CAMs. In this paper, we present a rational design of calcination for scalable production of Ni-based CAMs. We start with an overview of the current understanding and knowledge gaps hindering rational process design and scaling-up of the calcination process. Then with specific examples, we demonstrate how to tackle those fundamental challenges through in situ characterization and multiscale modeling. Finally, we conclude by providing perspectives on the remaining challenges and emerging opportunities in commercial development of Ni-based cathodes, calling for more endeavors in this field.

25 ENERGY STORAGE↗
DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2026

Reverse Logistics Ev Battery Recycling Agent Base Model

This represents the initial regional tier of the electric vehicle (EV) battery recycling agent base model. Through this model, we can ascertain the number of EV purchases at both the state and regional levels. We employ census data to develop a diverse household profile to inform decisions regarding the acquisition of new or used EVs. The number of EV purchases at the state level will affect the future demand for recycling, reuse, and repurposing of end-of-life EV batteries. Additionally, tax credits, EV rebate programs, and the financial capacity of households will influence the number of EV purchases, thereby further impacting the demand for EV battery recycling.

Alam, Lamia [Idaho National Laboratory (INL), Idah↗
DOE OSTI2023

Continuum-Level Modeling of Li-Ion Battery SEI by Upscaling Atomistically Informed Reaction Mechanisms

Understanding and controlling solid-electrolyte interphase (SEI) formation to stabilize cell performance is a significant challenge for next-generation Li-ion battery technologies. In recent years, computational modeling has become an essential tool in providing fundamental insights into SEI properties and dynamics. However, neither atomistic nor continuum-level approaches alone can capture the complexities of SEI chemistry across all relevant length and time scales. In this work, a continuum-level model is developed that is informed by reaction mechanisms obtained from first-principle calculations. The atomistically informed continuum-level model is used to understand electrolyte degradation, including the decomposition of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and fluoroethylene carbonate (FEC). The model presented here is the most chemically complex continuum-level SEI model in the literature to date. The SEI model is calibrated against experimental irreversible leakage currents and shows qualitative agreement with expected SEI growth trends. The model framework is expected to accelerate fundamental understanding of SEI formation, facilitate mechanism development feedback, and dynamically interact with experimental insights.

continuum-level model↗
DOE OSTI2026

Mechanical, Electrochemical & Thermal Modeling of Electric Vehicle Batteries for Crash Simulation (CRADA CRD-19-00811 Final Report)

Under the proposed effort in partnership with Hyundai Motor Company (HMC), the National Laboratory of the Rockies (NLR) will cooperate with HMC to develop mathematical models for battery cells and modules for simulating abuse response in batteries subject to the type of mechanical crushing that can occur in a full motor vehicle crash.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A Three-Dimensional, Thermodynamically and Variationally Consistent, Fully Coupled, Electro-Chemo-Thermo-Mechanical Model of Solid-State Batteries

Here we present a theoretical framework for high fidelity modeling of three-dimensional electro-chemo-thermo-mechanical coupled systems such as solid-state batteries. The framework provides a useful means of utilizing molecular level information to predict the overall performance of the system at the continuum level. The central statement of the theoretical framework is a thermodynamically consistent inf-sup problem. The total potential of the coupled system consists of the Helmholtz free energy, the electrical kinetic potential, the chemical potential, the Fourier potential, the chemical reaction potential, and the external power expenditure. All governing equations derive from variational principles. We also show that the resulting initial-boundary-value problem (IBVP) is consistent with existing, widely adopted equations for modeling batteries. Moreover, the IBVP can be naturally discretized using the (variational) finite element method to simulate practical solid-state batteries with arbitrary geometries. Several examples illustrate multiphysics coupling between pairs of the physical field equations, culminating in a comprehensive three-dimensional model of charge-discharge in a solid state battery.

25 ENERGY STORAGE↗
DOE OSTI2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.

25 ENERGY STORAGE↗
DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗
DOE OSTI2020

Modeling of deformation of battery cells using thick shell element formulation

Here, we describe a new approach for modeling nonlinear deformation and stress distribution of battery cells using a new thick shell finite element formulation with a through-thickness calculation of stresses and strains that satisfy equilibrium conditions. Battery cells are transversely layered materials that contain numerous thin layers in a repeating sequence. The layers are made of materials with significantly different mechanical responses. Explicit discretization of the layers is computationally impractical except for very small domains, while homogenized material models cannot account for stress and strain variations and partition through the thickness. The new formulation allows for calculation of the transverse and interlayer stresses based on the material properties of the individual cell layers. The application to problems where the transverse stresses have strong influence is also possible.

25 ENERGY STORAGE↗
DOE OSTI2021

Multi-fidelity electrochemical modeling of thermally activated battery cells

Thermally activated batteries undergo a series of coupled physical changes during activation that influence battery performance. These processes include energetic material burning, heat transfer, electrolyte phase change, capillary-driven two-phase porous flow, ion transport, electrochemical reactions, and electrical transport. Several of these processes are strongly coupled and have a significant effect on battery performance, but others have minimal impact or may be suitably represented by reduced-order models. Additionally, assessing the relative importance of these phenomena must be based on comparisons to a high-fidelity model including all known processes. In this work, we first present and demonstrate a high-fidelity, multi-physics model of electrochemical performance. This novel multi-physics model enables predictions of how competing physical processes affect battery performance and provides unique insights into the difficult-to-measure processes that happen during battery activation. We introduce four categories of model fidelity that include different physical simplifications, assumptions, and reduced-order models to decouple or remove costly elements of the simulation. Using this approach, we show an order-of-magnitude reduction in computational cost while preserving all design-relevant quantities of interest within 5 percent. The validity of this approach and these model reductions is demonstrated by comparison between results from the full fidelity model and the different reduced models.

25 ENERGY STORAGE↗
DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗
DOE OSTI2022

Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.

battery degradation↗
DOE OSTI2021

Rapid failure mode classification and quantification in batteries: A deep learning modeling framework

Unique, rapid identification and quantification of the dominant aging modes in lithium-ion batteries (LiBs) with early and non-specialized test data is a significant scientific challenge. Leveraging synthetic-data, deep-learning (DL) techniques have great potential to enable fast and robust classification and quantification of battery aging modes that produce different patterns of cell aging. This study, for the first time, presents a synthetic–data-based DL modeling framework for rapid and automatic classification and quantification of battery-aging modes and resultant aging with experimental validation. Availing synthetic dQ.dV -1 curves for ~26000 initial conditions and aging modes, the framework classified the dominant aging modes, for cells undergoing fast charge, in fewer than 100 cycles. Upon classification, the framework quantified the evolution of the aging modes, which were often nonuniform with cycling, for 22 gr/NMC532 pouch cells tested up to 600 cycles at different charging rates (1C–9C).

25 ENERGY STORAGE↗
DOE OSTI2022

Battery Energy Storage Scenario Analyses Using the Lithium-Ion Battery Resource Assessment (LIBRA) Model

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

25 ENERGY STORAGE↗
DOE OSTI2023

Techniques for controlling charging and/or discharging of batteries using a tanks-in-series model

In some embodiments, a battery management system is provided. The battery management system comprises a connector for electrically coupling a battery to the battery management system, at least one sensor configured to detect a battery state, a programmable chip configured to control at least one of charging and discharging of the battery, and a controller device. The controller device is configured to receive at least one battery state from the at least one sensor; provide the at least one battery state as input to a tanks-in-series model that represents the battery; and provide at least one output of the tanks-in-series model to the programmable chip for controlling at least one of charging and discharging of the battery.

25 ENERGY STORAGE↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗
DOE OSTI2025

BATMODS-lite [SWR-25-108]

Battery Analysis and Training Models for Optimization and Design Studies (BATMODS) is a Python package with an API for pre-built battery models. The original purpose of the package was to quickly generate synthetic data for machine learning models to train with. However, the models are generally useful for any battery simulations or analysis. BATMODS-lite includes the following: 1) A library and API for pre-built battery models 2) Kinetic/transport properties for common battery materials

Randall, Corey [National Laboratory of the Rockies↗