Engineering

K. Goebel, B. Saha, A. Saxena, J. Celaya, J. Christophersen

2008.7.25IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE

DOI: 10.1109/mim.2008.4579269

tlooto Summary

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 is used for this case study is shown.

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.

Citation format

GOEBEL, K., et al. Prognostics in battery health management. IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE, 2008, 11.