Lithium polymer battery state-of-charge estimation based on adaptive unscented kalman filter and support vector machine

IEEE Transactions on Power Electronics

Volumn 31, Issue 3, 2016, Pages 2226-2238

  Meng, Jinhao ^a   Luo, Guangzhao ^a   Gao, Fei ^b  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b UNIVERSITÉ DE TECHNOLOGIE DE BELFORT MONTBÉLIARD   (France)

Author keywords

Adaptive unscented Kalman filter (AUKF); Leastsquare support vector machine (LSSVM); Lithium polymer battery; Modeling; Moving window method; State of charge (SOC)

Indexed keywords

ALGORITHMS; BATTERY MANAGEMENT SYSTEMS; ELECTRIC BATTERIES; KALMAN FILTERS; LITHIUM; LITHIUM-ION BATTERIES; MODELS; NONLINEAR FILTERING; POLYMERS; SAMPLING; SECONDARY BATTERIES; SUPPORT VECTOR MACHINES;

ADAPTIVE UNSCENTED KALMAN FILTER (AUKF); INTERNAL CHARACTERISTICS; LEAST SQUARE SUPPORT VECTOR MACHINES; LEAST-SQUARE SUPPORT VECTOR MACHINE (LSSVM); MEASUREMENT EQUATIONS; MOVING WINDOW METHODS; STATE OF CHARGE; UNSCENTED KALMAN FILTER;

CHARGING (BATTERIES);

EID: 84949499235     PISSN: 08858993     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPEL.2015.2439578     Document Type: Article

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