{"id":"W2566774925","doi":"10.1109/vppc.2016.7791701","title":"IEEE VTS Motor Vehicles Challenge 2017 - Energy Management of a Fuel Cell/Battery Vehicle","year":2016,"lang":"en","type":"article","venue":"","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Energy management; Battery (electricity); Computer science; Fuel cells; Automotive engineering; Session (web analytics); MATLAB; Energy consumption; Fuel efficiency; Energy (signal processing); Simulation; Engineering management; Engineering; Operating system; Electrical engineering; World Wide Web","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":[0.003876994,0.0009393391,0.001092232,0.0007976597,0.001462384,0.002465406,0.00204653,0.004385958,0.007069868],"category_scores_gemma":[0.003234238,0.0002670402,0.0007540495,0.0005048934,0.0008835879,0.001903565,0.002138806,0.002244268,0.004272675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002348674,"about_ca_system_score_gemma":0.005535076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007965227,"about_ca_topic_score_gemma":0.01151724,"domain_scores_codex":[0.9976313,0.0003564286,0.00007742413,0.000156113,0.001301748,0.0004769878],"domain_scores_gemma":[0.9977624,0.0002114082,0.00006944066,0.0001063081,0.001381995,0.0004684021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006215301,0.0005668818,0.002652791,0.0007085043,0.00008750713,0.0009539567,0.0003360351,0.04211146,0.01642895,0.06101131,0.6819972,0.1925239],"study_design_scores_gemma":[0.00009393144,0.0007292739,0.001843198,0.0002668269,0.00004205532,0.0004880692,0.0006495997,0.0540539,0.01331908,0.01778945,0.9106389,0.00008574474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1230354,0.02200417,0.209419,0.09886069,0.04060344,0.002238747,0.006953251,0.003922969,0.4929623],"genre_scores_gemma":[0.5175366,0.01038768,0.05772906,0.008016248,0.005738526,0.001198803,0.01887048,0.001145431,0.3793771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007965227,"threshold_uncertainty_score":0.023651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231468622759523,"score_gpt":0.191768934119561,"score_spread":0.1794542478919657,"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."}}