A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile

Applied Energy

Volumn 164, Issue , 2016, Pages 387-399

  Yang, Fangfang ^a   Xing, Yinjiao ^b   Wang, Dong ^a   Tsui, Kwok Leung ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

Degradation; Extended Kalman filter; Lithium ion batteries; Particle filter; State of charge; Unscented Kalman filter

Indexed keywords

ALGORITHMS; CHARGING (BATTERIES); DEGRADATION; DYNAMIC LOADS; ELECTRIC BATTERIES; EXTENDED KALMAN FILTERS; KALMAN FILTERS; LITHIUM ALLOYS; LITHIUM COMPOUNDS; LITHIUM-ION BATTERIES; MONTE CARLO METHODS; NONLINEAR FILTERING; SECONDARY BATTERIES; STRESS ANALYSIS; UNCERTAINTY ANALYSIS;

BATTERY DEGRADATION; COMPARATIVE STUDIES; COMPARISON RESULT; FILTERING ALGORITHM; PARTICLE FILTER; STATE OF CHARGE; TRACKING ACCURACY; UNSCENTED KALMAN FILTER;

BATTERY MANAGEMENT SYSTEMS;

ALGORITHM; COMPARATIVE STUDY; CONVERGENCE; DEGRADATION; ELECTRIC VEHICLE; ESTIMATION METHOD; KALMAN FILTER; LITHIUM; PERFORMANCE ASSESSMENT;

EID: 84951095944     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.11.072     Document Type: Article

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