Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 4, 2016, Pages 2645-2656

  Hu, Xiaosong ^a,b   Jiang, Jiuchun ^a   Cao, Dongpu ^c   Egardt, Bo ^d  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b 109 McLaughlin Hall   (United States)

^c CRANFIELD UNIVERSITY   (United Kingdom)

^d CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Bayesian Inference; Electric Vehicle; Energy Storage; Health Monitoring; Lithium ion Battery; Machine Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BATTERY MANAGEMENT SYSTEMS; BAYESIAN NETWORKS; DIGITAL STORAGE; ELECTRIC BATTERIES; ELECTRIC VEHICLES; ENERGY STORAGE; ENTROPY; INFERENCE ENGINES; LEARNING SYSTEMS; LITHIUM ALLOYS; SECONDARY BATTERIES; SUPPORT VECTOR MACHINES; VEHICLES;

ANALYTICAL INTEGRATION; BAYESIAN INFERENCE; BOOTSTRAP SAMPLINGS; HEALTH MONITORING; MODEL CONSTRUCTION; POLYNOMIAL MODELING; PREDICTIVE MODELING; REMAINING USEFUL LIVES;

LITHIUM-ION BATTERIES;

EID: 84963719958     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2015.2461523     Document Type: Article

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