{"id":"W2885578090","doi":"10.1016/j.jpowsour.2018.06.104","title":"State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach","year":2018,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":791,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Canada Research Chairs; Government of Canada; University of Wisconsin-Madison; Nvidia; Canada Excellence Research Chairs, Government of Canada; U.S. Environmental Protection Agency","keywords":"Battery (electricity); State of charge; Artificial neural network; Computer science; Process (computing); Artificial intelligence; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620022038606214,"score_gpt":0.2636653438890747,"score_spread":0.2474651235030126,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}