Search NASA⌕ Search

SEARCH · Search NASA

Results for “MSMD”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

Efficient and Extensible Quasi-Explicit Modular Nonlinear Multiscale Battery Model: GH-MSMD

Complex physics and long computation time hinder the adoption of computer aided engineering models in the design of large-format battery cells and systems. A modular, efficient battery simulation model -- the multiscale multidomain (MSMD) model -- was previously introduced to aid the scale-up of Li-ion material and electrode designs to complete cell and pack designs, capturing electrochemical interplay with 3-D electronic current pathways and thermal response. Here, this paper enhances the computational efficiency of the MSMD model using a separation of time-scales principle to decompose model field variables. The decomposition provides a quasi-explicit linkage between the multiple length-scale domains and thus reduces time-consuming nested iteration when solving model equations across multiple domains. In addition to particle-, electrode- and cell-length scales treated in the previous work, the present formulation extends to bus bar- and multi-cell module-length scales. We provide example simulations for several variants of GH electrode-domain models.

30 DIRECT ENERGY CONVERSION↗

Molecular Symmetry-Mixed Dichroism in Double Photoionization of $H 2 $

Dichroism in double photoionization of $H_2$ molecules by elliptically polarized extreme ultraviolet pulses is formulated analytically as a sum of atomiclike dichroism (AD) and molecular symmetry-mixed dichroism (MSMD) terms. The MSMD originates from an interplay of $^1Σ$$^+\atop{u}$ and $^1Π$$^+\atop{u}$ continuum molecular ionization amplitudes. For detection geometries in which the AD vanishes, numerical results for the sixfold differential probabilities for opposite pulse helicities show that the MSMD is significant in the electron momentum and angular distributions and is controllable by the ellipticity.

74 ATOMIC AND MOLECULAR PHYSICS↗

Accelerating Development of EV Batteries Through Computer-Aided Engineering (Presentation)

The Department of Energy's Vehicle Technology Program has launched the Computer-Aided Engineering for Automotive Batteries (CAEBAT) project to work with national labs, industry and software venders to develop sophisticated software. As coordinator, NREL has teamed with a number of companies to help improve and accelerate battery design and production. This presentation provides an overview of CAEBAT, including its predictive computer simulation of Li-ion batteries known as the Multi-Scale Multi-Dimensional (MSMD) model framework. MSMD's modular, flexible architecture connects the physics of battery charge/discharge processes, thermal control, safety and reliability in a computationally efficient manner. This allows independent development of submodels at the cell and pack levels.

25 ENERGY STORAGE↗

Multiscale Multiphysics Lithium-Ion Battery Model with Multidomain Modular Framework

Lithium-ion batteries (LIBs) powering recent wave of personal ubiquitous electronics are also believed to be a key enabler of electrification of vehicle powertrain on the path toward sustainable transportation future. Over the past several years, National Renewable Energy Laboratory (NREL) has developed the Multi-Scale Multi-Domain (MSMD) model framework, which is an expandable platform and a generic modularized flexible framework resolving interactions among multiple physics occurring in varied length and time scales in LIB[1]. NREL has continued to enhance the functionality of the framework and to develop constituent models in the context of the MSMD framework responding to U.S. Department of Energy's CAEBAT program objectives. This talk will introduce recent advancements in NREL's LIB modeling research in regards of scale-bridging, multi-physics integration, and numerical scheme developments.

25 ENERGY STORAGE↗

Deep Reinforcement Learning based Routing in an Air-to-Air Ad-hoc Network

This paper studies the Multiple Sources and Multiple Destinations (MSMD) routing problem in a dynamic Air-to-Air Ad-hoc Network (AAAN). We consider a spectrum limited scenario where multiple links have to share the same frequency channel so that co-channel interference becomes inevitable. As a result, routing decisions and spectrum access are coupled and must be jointly considered. This paper proposes a deep Q-learning based algorithm to find an optimal routing and channel selection strategy that minimizes the end-to-end communication delay. Specifically, under the assumption that only local information is available to every node, the Deep Q-Network (DQN) is trained offline to learn the optimal routing and channel selection strategy. After the trained DQN is implemented in every node, multiple relay nodes can simultaneously determine their next-hop relay and channel selections in real-time. Simulation results demonstrate the efficacy of our proposed algorithm.

AAAN↗

Deep Reinforcement Learning based Routing in an Air-to-Air Ad-hoc Network

This paper studies the Multiple Sources and Multiple Destinations (MSMD) routing problem in a dynamic Air-to-Air Ad-hoc Network (AAAN). We consider a spectrum limited scenario where multiple links have to share the same frequency channel so that co-channel interference becomes inevitable. As a result, routing decisions and spectrum access are coupled and must be jointly considered. This paper proposes a deep Q-learning based algorithm to find an optimal routing and channel selection strategy that minimizes the end-to-end communication delay. Specifically, under the assumption that only local information is available to every node, the Deep Q-Network (DQN) is trained offline to learn the optimal routing and channel selection strategy. After the trained DQN is implemented in every node, multiple relay nodes can simultaneously determine their next-hop relay and channel selections in real-time. Simulation results demonstrate the efficacy of our proposed algorithm.

AAAN↗

Computer Aided Battery Engineering Consortium

A multi-national lab collaborative team was assembled that includes experts from academia and industry to enhance recently developed Computer-Aided Battery Engineering for Electric Drive Vehicles (CAEBAT)-II battery crush modeling tools and to develop microstructure models for electrode design - both computationally efficient. Task 1. The new Multi-Scale Multi-Domain model framework (GH-MSMD) provides 100x to 1,000x computation speed-up in battery electrochemical/thermal simulation while retaining modularity of particles and electrode-, cell-, and pack-level domains. The increased speed enables direct use of the full model in parameter identification. Task 2. Mechanical-electrochemical-thermal (MECT) models for mechanical abuse simulation were simultaneously coupled, enabling simultaneous modeling of electrochemical reactions during the short circuit, when necessary. The interactions between mechanical failure and battery cell performance were studied, and the flexibility of the model for various batteries structures and loading conditions was improved. Model validation is ongoing to compare with test data from Sandia National Laboratories. The ABDT tool was established in ANSYS. Task 3. Microstructural modeling was conducted to enhance next-generation electrode designs. This 3- year project will validate models for a variety of electrodes, complementing Advanced Battery Research programs. Prototype tools have been developed for electrochemical simulation and geometric reconstruction.

25 ENERGY STORAGE↗

NREL-Led Team Improves and Accelerates Battery Design [Fact Sheet]

The National Renewable Energy Laboratory (NREL) is leading some of the best minds from U.S. auto manufacturers, battery developers, and automotive simulation tool developers in a $\$$20 million project to accelerate the development of battery packs and thus the wider adoption of electric-drive vehicles. The Computer-Aided Engineering for Electric Drive Vehicle Batteries (CAEBAT) collaboration is developing sophisticated software tools to help improve and accelerate battery design and boost the performance and consumer appeal of electric-drive vehicles with the ultimate goal of diminishing petroleum consumption and polluting emissions.

25 ENERGY STORAGE↗

Modeling Lithium Ion Battery Safety: Venting of Pouch Cells

This report documents the successful completion of the NREL July milestone entitled “Modeling Lithium-Ion Battery Safety - Complete Case-Studies on Pouch Cell Venting,” as part of the 2013 Vehicle Technologies Annual Operating Plan with the U.S. Department of Energy (DOE). This work aims to bridge the gap between materials modeling, usually carried out at the sub-continuum scale, and the

25 ENERGY STORAGE↗

Energy Storage Annual Progress Report for FY15

The Energy Storage research and development (R&D) subprogram within the DOE Vehicle Technologies Office (VTO) provides support and guidance for projects focusing on batteries for plug-in electric vehicles (PEVs) in support of the EV Everywhere Grand Challenge. PEVs could have a significant impact on the nation's goal of reducing dependence on imported oil and gaseous pollutant emissions. The Energy Storage program targets overcoming technical barriers to enable market success, including: (1) significantly reducing battery cost; (2) increasing battery performance (power, energy, durability); (3) reducing battery weight and volume; and (4) increasing battery tolerance to abusive conditions such as short circuit, overcharge, and crush. The National Renewable Energy Laboratory (NREL) supports the VTO's Energy Storage program by evaluating the thermal performance of cells and packs, developing electrochemical-thermal models to accelerate the design cycle for developing batteries, investigating the behavior of lithium-ion batteries under abuse conditions such as crush, enhancing the durability of electrodes by coatings such as atomic layer deposition, synthesis of materials for higher energy density batteries, and conducting techno-economic analysis of batteries in various electric-drive vehicles. This report describes the progress made by NREL on the research and development projects funded by the DOE VTO Energy Storage subprogram in FY15.

25 ENERGY STORAGE↗

Coupled Mechanical-Electrochemical-Thermal Modeling for Accelerated Design of EV Batteries; NREL (National Renewable Energy Laboratory)

The physical and chemical phenomena occurring in a battery are many and complex and in many different scales. Without a better knowledge of the interplay among the multi-physics occurring across the varied scales, it is very challenging and time consuming to design long-lasting, high-performing, safe, affordable large battery systems, enabling electrification of the vehicles and modernization of the grid. The National Renewable Energy Laboratory, a U.S. Department of Energy laboratory, has been developing thermal and electrochemical models for cells and battery packs. Working with software producers, carmakers, and battery developers, computer-aided engineering tools have been developed that can accelerate the electrochemical and thermal design of batteries, reducing time to develop and optimize them and thus reducing the cost of the system. In the past couple of years, we initiated a project to model the mechanical response of batteries to stress, strain, fracture, deformation, puncture, and crush and then link them to electrochemical and thermal models to predict the response of a battery. This modeling is particularly important for understanding the physics and processes that happen in a battery during a crush-inducing vehicle crash. In this paper, we provide an overview of electrochemical-thermal-mechanical models for battery system understanding and designing.

25 ENERGY STORAGE↗