Journal
JOURNAL OF POWER SOURCES
Volume 196, Issue 15, Pages 6007-6014Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2011.03.101
Keywords
Prognostics; Health monitoring; Li-ion battery; Estimation; Prediction; RUL
Funding
- NSF Industry/University Cooperative Research Center (I/UCRC) for Intelligent Maintenance Systems (IMS) at the University of Cincinnati
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The functionality and reliability of Li-ion batteries as major energy storage devices have received more and more attention from a wide spectrum of stakeholders, including federal/state policymakers, business leaders, technical researchers, environmental groups and the general public. Failures of Li-ion battery not only result in serious inconvenience and enormous replacement/repair costs, but also risk catastrophic consequences such as explosion due to overheating and short circuiting. In order to prevent severe failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion batteries, with an emphasis on fault detection, correction and remaining-useful-life prediction, must be achieved. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques, algorithms and models used for stale-of-charge (SOC) estimation, current/voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction. (C) 2011 Elsevier B.V. All rights reserved.
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