Cited by 594Battery Modeling and ManagementReliability and Maintenance OptimizationFault Detection and Control Systems

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Prognostics in Battery Health Management

Goebel, Kai, Saha, Bhaskar, Saxena, Abhinav, Celaya, Jose, Christophersen, Jon·2008·IEEE Instrumentation & Measurement Magazine

DOI 10.1109/mim.2008.4579269

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Abstract

In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.

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Authors: Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J.
Affiliations: Ames Research Center (US)
Idaho National Laboratory (US)
ORCID: Goebel, Kai
Saha, Bhaskar
Saxena, Abhinav
Celaya, Jose
Venue: IEEE Instrumentation & Measurement Magazine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2008-08
Volume: 11
Issue: 4
Pages: 33-40
ISSN: 1094-6969 (ISSN-L)
DOI: 10.1109/mim.2008.4579269
OpenAlex: W2115233498
Cited by: 594
References: 11
Citations by year: 2026: 21 · 2025: 46 · 2024: 46 · 2023: 62 · 2022: 46 · 2021: 59 · 2020: 40 · 2019: 48 · 2018: 39 · 2017: 29 · 2016: 26 · 2015: 28 · 2014: 34 · 2013: 24 · 2012: 17
FWCI: 22.97
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Instrumentation & Measurement Magazine (publishedVersion)
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords: prognostics and health management, battery health management, lithium-ion cells, 18650 cells, battery degradation prediction, regression analysis, classification, state estimation

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Prognostics in Battery Health Management

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