@inproceedings{4accd748fba34c439fccf842786664dc,
title = "Advanced filtering technique for battery failure prognostics",
abstract = "Prognostics and health management (PHM) has become a prominent field with an emphasis on the analysis of dynamic system degradation, with the objective of enhancing decision making for contingency improvement. The fusion of data-driven and model-based approaches have caused applications of PHM to require extensive concentration in forecasting and filtering techniques. In recent studies, the combination of dynamic system modeling and filtering methods have shown to be extremely effective for PHM applications. Moreover, methods utilizing artificial intelligence (AI) for system modeling, combined with a filtering method for estimation, have shown promising results. This study aims to provide insight to the optimal method of modeling and estimation particularly for PHM concentrations and develop a general prognostics platform using advanced filtering techniques coupled with artificial intelligence based dynamic system modelers. The specific application is a battery dynamic system, with the parameters of interest being state of charge (SOC) and state of health (SOH).",
keywords = "Battery, Filter, Prognostics, State of Charge, State of Health",
author = "Sara Kohtz and Pingfeng Wang",
note = "Funding Information: This work was partially supported by Office of Naval Research (ONR) through the Defense University Research-to-Adoption (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 also 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} Proceedings of the 2020 IISE Annual. All Rights Reserved.; 2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020 ; Conference date: 01-11-2020 Through 03-11-2020",
year = "2020",
language = "English (US)",
series = "Proceedings of the 2020 IISE Annual Conference",
publisher = "Institute of Industrial and Systems Engineers, IISE",
pages = "1503--1508",
editor = "L. Cromarty and R. Shirwaiker and P. Wang",
booktitle = "Proceedings of the 2020 IISE Annual Conference",
}