{"id":"W3111965818","doi":"10.1109/tits.2020.3038274","title":"An Optimal Battery Charging Algorithm in Electric Vehicle-Assisted Battery Swapping Environments","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Research Foundation","keywords":"Markov decision process; Battery (electricity); Quality of service; Computer science; Charging station; Electricity; Schedule; Mathematical optimization; Profit (economics); Markov process; Electric vehicle; Engineering; Electrical engineering; Computer network; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000407672,0.0004658494,0.0008053285,0.00029861,0.0004587283,0.0007390974,0.0008961337,0.0007144384,0.002264621],"category_scores_gemma":[0.00106875,0.0003475994,0.000284782,0.0005203987,0.0004674182,0.0008142343,0.0007778117,0.0005343505,0.0002440298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007959828,"about_ca_system_score_gemma":0.001580372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006388091,"about_ca_topic_score_gemma":0.004741051,"domain_scores_codex":[0.9997235,0.00007222538,0.00001179886,0.00005473238,0.00005142975,0.00008627659],"domain_scores_gemma":[0.9996672,0.0001594325,0.00003815375,0.00002071517,0.00007584588,0.00003876117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001167929,0.00005819918,0.0004045726,0.00003459373,0.0000140891,0.00005610005,0.0000470341,0.9457252,0.001457664,0.009767017,0.001722469,0.04059615],"study_design_scores_gemma":[0.00001155106,0.0000146269,0.00004416601,0.000001674537,0.000002016315,0.00001122395,0.0000119666,0.9971398,0.0002178439,0.00224845,0.0002941425,0.000002506586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08335821,0.0004386009,0.9061347,0.0004652299,0.00007786674,0.00008896089,0.00008877298,0.0005363365,0.008811355],"genre_scores_gemma":[0.9160187,0.0001939132,0.08024132,0.0001055192,0.00002396513,0.00007031319,0.00007921585,0.00004673169,0.003220366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006388091,"threshold_uncertainty_score":0.01270181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441326470846582,"score_gpt":0.2115690988961814,"score_spread":0.1971558341877156,"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."}}