@inproceedings{90cf3fba44ce4fb8947a1cfc3f448e81,
title = "A comparison study of machine learning enabled filtering methods for battery management",
abstract = "Prognostics and health management has become a prominent field for the analyses of dynamic system degradation. Specifically, methods for forecasting remaining useful life have been studied extensively, including some hybrid approaches that have indicated successful results. Mainly, a combination of machine learning and filtering techniques have shown to be the most effective. Currently, there exists a need to determine an optimal general method for remaining useful life estimation in complex systems. This paper focuses on a comparison between successful hybrid approaches. The methods are applied to modeling capacity degradation in lithium-ion batteries, with the NASA dataset utilized for this study.",
keywords = "Battery, Filtering, Health management, Machine Learning, Prognostics, State of Charge, State of Health",
author = "Sara Kohtz and Pingfeng Wang",
note = "Funding Information: ACKNOWLEDGMENT This work was partially supported by Office of Naval Research (ONR) through the Defense University Research-toAdoption (DURA) Initiative (N00014-18-S-F004) and the supplement project from the National Science Foundation (NSF) to the Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS) with cooperative agreement EEC-1449548. The authors would like to thank the Prognostics Center of Excellence at NASA Ames Research Center for the availability of the dataset on battery degradation. Funding Information: This work was partially supported by Office of Naval Research (ONR) through the Defense University ResearchtoAdoption (DURA) Initiative (N00014-18-S-F004) and the supplement project from the National Science Foundation (NSF) to the Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS) with cooperative agreement EEC-1449548. The authors would like to thank the Prognostics Center of Excellence at NASA Ames Research Center for the availability of the dataset on battery degradation. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 ; Conference date: 08-06-2020 Through 10-06-2020",
year = "2020",
month = jun,
doi = "10.1109/ICPHM49022.2020.9187029",
language = "English (US)",
series = "Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM",
publisher = "Prognostics and Health Management Society",
editor = "Indranil Roychoudhury and Celaya, {Jose R.} and Abhinav Saxena",
booktitle = "2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020",
}