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7. Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data.

Author

Bae, Yong Seok, Lee, Sungwon, and Moon, Janghyuk

Subjects
REMAINING useful life, CONVOLUTIONAL neural networks, LITHIUM-ion batteries, ENERGY storage, ELECTRIC vehicles, DEEP learning
Abstract

This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries' remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overcomes these limitations, predicting charging profiles and discharge capacity for untrained, low-rate conditions using only high-rate charging data. The Seq2Seq model demonstrated superior performance with low error and high curve-fitting accuracy for 1C and 1.2C untrained data. Unlike traditional models, it predicts complete charging profiles (voltage, current, temperature) for subsequent cycles, offering a comprehensive view of battery degradation. This method significantly reduces battery life testing time while maintaining high prediction accuracy. The findings have important implications for lithium-ion battery development, potentially accelerating advancements in electric vehicle technology and energy storage. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Everlasting Living and Breathing Gyroid 3D Network in Si@SiOx/C Nanoarchitecture for Lithium Ion Battery

Author

Lee, Jaewoo, Moon, Janghyuk, Han, Sanga, Kim, Junyoung, Malgras, Victor, Heo, Yoon-Uk, Kim, Hansu, Lee, Sang-Min, Liu, Hua-Kun, Dou, Shi Xue, Yamauchi, Yusuke, Park, Min-Sik, Kim, Jung Ho, Lee, Jaewoo, Moon, Janghyuk, Han, Sanga, Kim, Junyoung, Malgras, Victor, Heo, Yoon-Uk, Kim, Hansu, Lee, Sang-Min, Liu, Hua-Kun, Dou, Shi Xue, Yamauchi, Yusuke, Park, Min-Sik, and Kim, Jung Ho

Abstract

Silicon-based materials are the most promising candidates to surpass the capacity limitation of conventional graphite anode for lithium ion batteries. Unfortunately, Si-based materials suffer from poor cycling performance and dimensional instability induced by the large volume changes during cycling. To resolve such problems, nanostructured silicon-based materials with delicately controlled microstructure and interfaces have been intensively investigated. Nevertheless, they still face problems related to their high synthetic cost and their limited electrochemical properties and thermal stability. To overcome these drawbacks, we demonstrate the strategic design and synthesis of a gyroid three-dimensional network in a Si@SiOx/C nanoarchitecture (3D-Si@SiOx/C) with synergetic interaction between the computational prediction and the synthetic optimization. This 3D-Si@SiOx/C exhibits not only excellent electrochemical performance due to its structural stability and superior ion/electron transport but also enhanced thermal stability due to the presence of carbon, which was formed by a cost-effective one-pot synthetic route. We believe that our rationally designed 3D-Si@SiOx/C will lead to the development of anode materials for the next-generation lithium ion batteries.

Published
2019

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