A generic model-free approach for lithium-ion battery health management

Applied Energy

Volumn 135, Issue , 2014, Pages 247-260

  Bai, Guangxing ^a   Wang, Pingfeng ^a   Hu, Chao ^b,c   Pecht, Michael ^b  

^a Wichita State University   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

^c MEDTRONIC INC   (United States)

Author keywords

Artificial neural network; Battery; Health management; Kalman filter; State of charge; State of health

Indexed keywords

CHARGING (BATTERIES); ELECTRIC BATTERIES; EQUATIONS OF STATE; INFORMATION MANAGEMENT; KALMAN FILTERS; LITHIUM; LITHIUM ALLOYS; LITHIUM BATTERIES; NEURAL NETWORKS; SECONDARY BATTERIES;

ARTIFICIAL NEURAL NETWORK MODELING; BATTERY; DUAL EXTENDED KALMAN FILTER; HEALTH MANAGEMENT; STATE OF CHARGE; STATE OF HEALTH; STATE SPACE EQUATION; TRAINED NEURAL NETWORKS;

BATTERY MANAGEMENT SYSTEMS;

ARTIFICIAL NEURAL NETWORK; ION; KALMAN FILTER; LITHIUM; MANAGEMENT PRACTICE; MODEL TEST; RISK FACTOR;

EID: 84907176493     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2014.08.059     Document Type: Article

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