@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 = "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. 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.; 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",
}