Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

Neural Computing and Applications

Volumn 25, Issue 3-4, 2014, Pages 557-572

  Liu, Datong ^a   Luo, Yue ^b   Liu, Jie ^c   Peng, Yu ^a   Guo, Limeng ^a   Pecht, Michael ^d  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b Beijing Institute of Electric System Engneering   (China)

^c CARLETON UNIVERSITY   (Canada)

^d UNIVERSITY OF MARYLAND   (United States)

Author keywords

Data driven prognostics; Fusion prognostics; Lithium ion battery; ND AR; RPF

Indexed keywords

CHARGING (BATTERIES); ELECTRIC BATTERIES; FORECASTING; IONS; LIFE CYCLE; LITHIUM; LITHIUM ALLOYS; LITHIUM COMPOUNDS; MONTE CARLO METHODS; NASA; NONLINEAR ANALYSIS; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; SECONDARY BATTERIES; SYSTEMS ENGINEERING; TIME SERIES;

ACCELERATED DEGRADATION; DATA-DRIVEN PROGNOSTICS; DIFFERENT OPERATING CONDITIONS; LIFE CYCLE ENGINEERING; PROBABILITY DENSITY DISTRIBUTION; REMAINING USEFUL LIVES; TIME SERIES PREDICTION; UNCERTAINTY REPRESENTATION;

LITHIUM-ION BATTERIES;

EID: 84888361339     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-013-1520-x     Document Type: Article

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