Journal
JOURNAL OF POWER SOURCES
Volume 239, Issue -, Pages 680-688Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jpowsour.2012.11.146
Keywords
Lithium-ion battery; Battery management system; State of health; Remaining useful life; Support vector machine
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The accurate estimation of state of health (SOH) and a reliable prediction of the remaining useful life (RUL) of Lithium-ion (Li-ion) batteries in hybrid and electrical vehicles are indispensable for safe and lifetime-optimized operation. The SOH is indicated by internal battery parameters like the actual capacity value. Furthermore, this value changes within the battery lifetime, so it has to be monitored on-board the vehicle. In this contribution, a new data-driven approach for embedding diagnosis and prognostics of battery health in alternative power trains is proposed. For the estimation of SOH and RUL, the support vector machine (SVM) as a well-known machine learning method is used. As the estimation of SOH and RUL is highly influenced by environmental and load conditions, the SVM is combined with a new method for training and testing data processing based on load collectives. For this approach, an intensive measurement investigation was carried out on Li-ion power-cells aged to different degrees ensuring a large amount of data. (C) 2012 Elsevier B.V. All rights reserved.
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