A novel Gaussian model based battery state estimation approach: State-of-Energy

Applied Energy

Volumn 151, Issue , 2015, Pages 41-48

  He, HongWen ^a   Zhang, YongZhi ^a   Xiong, Rui ^a   Wang, Chun ^a,b  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b SICHUAN UNIVERSITY OF SCIENCE AND ENGINEERING   (China)

Author keywords

Akaike information criterion; Central difference Kalman filter; Electric vehicles; Gaussian model; Lithium ion battery; State of energy

Indexed keywords

ELECTRIC BATTERIES; ELECTRIC VEHICLES; ESTIMATION; GAUSSIAN DISTRIBUTION; GENETIC ALGORITHMS; HYSTERESIS; KALMAN FILTERS; LITHIUM ALLOYS; LITHIUM COMPOUNDS; LITHIUM-ION BATTERIES; OPEN CIRCUIT VOLTAGE; SECONDARY BATTERIES;

AKAIKE INFORMATION CRITERION; CENTRAL DIFFERENCE KALMAN FILTER; COMPARATIVE ANALYSIS; ELECTRIC VEHICLES (EVS); ESTIMATION APPROACHES; GAUSSIAN MODEL; PREDICTION PRECISION; STATE OF ENERGY;

BATTERY MANAGEMENT SYSTEMS;

AKAIKE INFORMATION CRITERION; CATION; ELECTRIC VEHICLE; ERROR ANALYSIS; GAUSSIAN METHOD; GENETIC ALGORITHM; KALMAN FILTER; LITHIUM; NUMERICAL MODEL;

EID: 84928538311     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.04.062     Document Type: Article

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