Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.
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ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 26–29, 2018
Quebec City, Quebec, Canada
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5175-3
PROCEEDINGS PAPER
Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks
M. K. Sadoughi,
M. K. Sadoughi
Iowa State University, Ames, IA
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Xiangyi Chen,
Xiangyi Chen
University of Minnesota Twin Cities, Minneapolis, MN
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Mingyi Hong,
Mingyi Hong
University of Minnesota Twin Cities, Minneapolis, MN
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Chao Hu
Chao Hu
Iowa State University, Ames, IA
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Sheng Shen
Iowa State University, Ames, IA
M. K. Sadoughi
Iowa State University, Ames, IA
Xiangyi Chen
University of Minnesota Twin Cities, Minneapolis, MN
Mingyi Hong
University of Minnesota Twin Cities, Minneapolis, MN
Chao Hu
Iowa State University, Ames, IA
Paper No:
DETC2018-86347, V02AT03A058; 8 pages
Published Online:
November 2, 2018
Citation
Shen, S, Sadoughi, MK, Chen, X, Hong, M, & Hu, C. "Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks." Proceedings of the ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2A: 44th Design Automation Conference. Quebec City, Quebec, Canada. August 26–29, 2018. V02AT03A058. ASME. https://doi.org/10.1115/DETC2018-86347
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