@article{
 title = {Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks},
 type = {article},
 year = {2014},
 keywords = {Battery management system,Dynamic Bayesian Network,Lithium-ion battery,State of health},
 pages = {576-583},
 volume = {267},
 websites = {http://www.sciencedirect.com/science/article/pii/S0378775314007939},
 month = {12},
 id = {2bd03643-6f6e-3455-80aa-0f76b3dec2ad},
 created = {2015-04-11T20:41:36.000Z},
 accessed = {2015-01-12},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.},
 bibtype = {article},
 author = {He, Zhiwei and Gao, Mingyu and Ma, Guojin and Liu, Yuanyuan and Chen, Sanxin},
 doi = {10.1016/j.jpowsour.2014.05.100},
 journal = {Journal of Power Sources}
}

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This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.","bibtype":"article","author":"He, Zhiwei and Gao, Mingyu and Ma, Guojin and Liu, Yuanyuan and Chen, Sanxin","doi":"10.1016/j.jpowsour.2014.05.100","journal":"Journal of Power Sources","bibtex":"@article{\n title = {Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks},\n type = {article},\n year = {2014},\n keywords = {Battery management system,Dynamic Bayesian Network,Lithium-ion battery,State of health},\n pages = {576-583},\n volume = {267},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378775314007939},\n month = {12},\n id = {2bd03643-6f6e-3455-80aa-0f76b3dec2ad},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-01-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. 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