| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | July 26, 2017 |
| Latest Amendment Date: | July 26, 2017 |
| Award Number: | 1747544 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Maria Zemankova
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2017 |
| End Date: | December 31, 2018 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $150,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
401 N. Grant Street West Lafayette IN US 47907-2021 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CONDENSED MATTER & MAT THEORY, Info Integration & Informatics, Data Cyberinfrastructure, Materials Eng. & Processing |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Nearly all portable electronic devices commonly used today -- cameras, phones, music players and the like -- rely on rechargeable Lithium-ion batteries. Improvements in the capabilities of these devices can be achieved by improving the design of these batteries. This work will produce new computational methods for designing batteries with desirable properties such as high power output and long lifespan. The new computational methods will use techniques that have successfully described complex volumetric structures (such as porous rocks and sponges) in computer graphics for film and games. These computer graphics techniques will be applied to describing the materials in batteries. Instead of focusing on finding volumetric structures that give the correct visual appearance, the new computational methods will focus on structures that produce the correct performance characteristics such as power density. The new volumetric descriptions will be used to generate a large number of potential volumetric materials, and these models will be characterized in terms of battery properties and performance. Using recently developed machine learning techniques, this large number of potential models will be converted into a form that is convenient to use in battery design. In addition to providing tools to create improved portable batteries, the new computational methods have the potential to be further extended and applied to other problems involving materials with complex volumetric structure such as understanding geologic measurements and designing conservation strategies for cultural heritage monuments and artifacts.
A straightforward approach to battery design is to theorize material microstructures, run forward simulations to assess their performance, and evaluate the results. However, simulations require hours (up to 50 hours on current multi-core systems for power density calculations), making forward simulation prohibitively expensive for iterative design. The design process can be dramatically improved if an inverse function is available that can produce a microstructure description given desired performance characteristics. Barriers to creating such an inverse function are the complexity of microstructure descriptions and the relationship between structure and performance. To create an inverse function, we need a microstructure description that is lower in dimension than a full enumeration of a high-resolution grid. A procedural model can provide such a lower dimensional description. The approach explored in this project for finding appropriate procedural models is based on combining and transforming models that have been successful in other problem domains to fit data from real battery material measurements. Given an appropriate procedural model, the design problem is reduced to determining the procedural model parameters that generate the input; a problem called "inverse procedural modeling". Even with a compact microstructure description, the problem is too complex to be mathematically inverted. Rather than attempt to find a mathematical function, machine learning (deep neural networks) are used. A database of microstructures and their performance characteristics will be populated synthetically with example microstructures computed from a large sampling of procedural model parameters. Forward simulations will be run on these samples to compute properties (tortuosity and area density) and performance characteristics (power and energy density.) Machine learning optimizations will then be used to find the relationship between model parameters and performance characteristics and this relationship will be used in the design process. The overall method of finding procedural models to fit data and then learning the relationships from synthetic data generated from the models brings the power of new data-driven approaches to the domain of battery design. The software, data and publications resulting from this project will be available at the project website (http://hpcg.purdue.edu/Eager2018/).
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Since the commercial introduction of Lithium-Ion Batteries (LIBs) in 1991, this energy storage technology has become ubiquitous in everyday life. Furthermore, emerging electric and hybrid electric vehicles markets, depleting fossil fuel reserves, and increasing concerns towards environmental pollution has led to a massive search for high performance LIBs, i.e., cells that deliver a maximal amount of energy without sacrificing delivered power. The internal structure of LIBs has a strong effect on their properties and, despite the vast body of previous work, there are many factors that do not allow an in-depth insight. Quantities such as porosity, reactive area density, and tortuosity, of volumetric structures represented as three-dimensional voxelized binary structures provide an important measure of macroscopic properties of porous electrodes, but are often difficult, numerically impractical, or slow to calculate.
We have developed, implemented, and made publicly available Fas๐: an open source software to calculate microstructural properties in porous electrodes for LIB applications, fuel cells, and the generality of reactive porous-based electrochemical systems. Fas๐ exploits the power of modern many core graphics processing units (GPUs), is combined with traditional CPU capabilities, and allows to measure structures of sized previously impossible to measure (786x786x786). Moreover, it provides a significant computational speedup of 33x, compared to existing state-of-the-art, currently available implementations. We used Fas๐ to analyze public, three-dimensional experimental data rapidly highlighting non-linear property-microstructure relations that emphasize new approaches to tailor the LIB microstructure to application-specific power density designs. Hundreds of real and tens of thousands synthetic volumetric structures have been analyzed and the data is publicly available for further research. We have generated pairs of millions of 2D cross sections of LIB together with their energy properties and behavior and we use it to train an AI system that will estimate energy characteristics of the LIB data directly from the 2D images. The proposed approach will enable battery processing operations experimentalists to estimate the properties and performance of the batteries by using a 2D (microscopy) image samples and predict the quality of the produced cell.
Moreover, by using Fas๐ we were able to quantify the topological properties of existing LIBs and significantly improve previous finding about their internal characteristics. By using Fas๐, and by analyzing on the order of 40,000 porous electrodes, three regimes of microstructural properties were identified: I) tunable, low power density structures, II) randomly distributed, oblate particles regime, and III) ordered, colloidal structures regime. Regime II is the traditionally explored space by commercially available batteries, even though it maximizes power losses. Here, we identified physically possible, never assembled architectures that have the potential of delivering the ideal combination of tortuosity and area density to optimize performance.
Last Modified: 03/27/2019
Modified by: Bedrich Benes
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