{"id":"W3035930691","doi":"10.1109/tii.2020.3003554","title":"Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Carleton University","funders":"","keywords":"Prognostics; Minification; Battery (electricity); Computer science; Automotive engineering; Fuel cells; Operating cost; Optimal decision; Power (physics); Operations research; Reliability engineering; Engineering; Artificial intelligence; Decision tree","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001063791,0.0002602205,0.0002542,0.0003494442,0.0001687972,0.0001266116,0.0003441814,0.0003654198,0.00007270659],"category_scores_gemma":[0.0001585868,0.0002710724,0.0001085464,0.0007854807,0.0000364388,0.0003473997,0.000002352187,0.001154854,0.00005825046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002168573,"about_ca_system_score_gemma":0.00009345789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.929604e-7,"about_ca_topic_score_gemma":4.660563e-7,"domain_scores_codex":[0.99842,0.00001810035,0.0005976709,0.0001489789,0.0003727611,0.0004424861],"domain_scores_gemma":[0.9982204,0.001193216,0.0000919767,0.0002906246,0.0000846988,0.0001190717],"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":[0.0003383049,0.00004098836,0.000001170972,0.00009178888,0.00001670814,0.000001631175,0.00007508118,0.8738026,0.00007295147,0.000005544603,0.001130318,0.1244229],"study_design_scores_gemma":[0.001168019,0.0006973709,7.47519e-7,0.0001095031,0.00002125476,0.000001232124,0.0002478441,0.9454743,0.05035013,0.000113362,0.001557537,0.0002586687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01938806,0.00001224182,0.9780455,0.0001264304,0.0003460019,0.0009394824,0.0001718249,0.0006071065,0.000363318],"genre_scores_gemma":[0.9549369,0.00005641528,0.04441482,0.0002533333,0.000107414,0.0001582054,0.00001408352,0.00005344309,0.000005445558],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9355488,"threshold_uncertainty_score":0.9999741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05163547890705724,"score_gpt":0.2929628028242062,"score_spread":0.2413273239171489,"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."}}