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.
|Original language||English (US)|
|Title of host publication||44th Design Automation Conference|
|Publisher||American Society of Mechanical Engineers (ASME)|
|State||Published - 2018|
|Event||ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018 - Quebec City, Canada|
Duration: Aug 26 2018 → Aug 29 2018
|Name||Proceedings of the ASME Design Engineering Technical Conference|
|Other||ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018|
|Period||8/26/18 → 8/29/18|
Bibliographical noteFunding Information:
This research was in part supported by the US National Science Foundation (NSF) Grant Nos. CNS-1566579 and ECCS-1611333, the NSF I/UCRC Center for e-Design, and the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) through the Midwest Transportation Center (MTC). Any opinions, findings or conclusions in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agencies. The authors would also like to express special thanks to Dr. Gaurav Jain and Dr. Hui Ye at Medtronic, Inc. for sharing the long-term cycling data for this study.
- Deep convolutional neural networks
- Li-ion battery
- State of Health