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Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

Metadata Updated: December 6, 2023

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and esti- mation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochem- ical processes in the form of equivalent electric circuit parame- ters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal frame- work. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.1

Index Terms—Battery health, Bayesian learning, particle filter, prognostics, relevance vector machine, remaining useful life.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date December 6, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 6, 2023
Publisher Dashlink
Identifier DASHLINK_682
Data First Published 2013-03-29
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 55740ab2-2904-4d10-9c38-2b115c44b4f0
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:029
Source Datajson Identifier True
Source Hash 083a00798d8390301767fff3ace030192842c3ed6eb1de05590d494039f29fea
Source Schema Version 1.1

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