Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles: Some Lessons Learned

Metadata Updated: July 17, 2020

Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast developmental cycle. However, no matter how easy it may seem, there are several pitfalls that one must watch out for while developing a data-driven prognostic algorithm. One such pitfall is the uncertainty inherent in the system. At each processing step uncertainties get compounded and can grow beyond control in predictions if not carefully managed during the various steps of the algorithms. This paper presents analysis from our preliminary development of data- driven algorithm for predicting end of discharge of Li-ion batteries using constant load experiment data and challenges faced when applying these algorithms to randomized variable loading profile as is the case in realistic applications. Lessons learned during the development phase are presented.

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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 August 1, 2018
Metadata Updated Date July 17, 2020
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
Publisher Dashlink
Unique Identifier DASHLINK_790
Miryam Strautkalns
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 1b6afcd9-9b27-47a9-be1f-1fc7a1fef5e4
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2013-06-19
Homepage URL
Data Last Modified 2020-01-29
Program Code 026:029
Source Datajson Identifier True
Source Hash 70ba505fa8c73ab8fd6b1ab358e59f6b181362e4
Source Schema Version 1.1

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