{"id":"W4293198341","doi":"10.1109/tte.2022.3200013","title":"Integrated Convex Speed Planning and Energy Management for Autonomous Fuel Cell Hybrid Electric Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Benchmark (surveying); Computer science; Energy (signal processing); Energy management; Computation; Automotive engineering; Hydrogen fuel; Mathematical optimization; Convex optimization; Simulation; Fuel cells; Regular polygon; Algorithm; Engineering; 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":[0.0004039821,0.0007928979,0.0005745871,0.0003508762,0.0003835113,0.0007452271,0.0007453884,0.0004433771,0.001353045],"category_scores_gemma":[0.0009146564,0.0005138235,0.0004863154,0.0004228006,0.0005153568,0.0007777613,0.0007103477,0.0006963458,0.0001919654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000626,"about_ca_system_score_gemma":0.001572942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426891,"about_ca_topic_score_gemma":0.01321003,"domain_scores_codex":[0.999693,0.00005875849,0.0000094697,0.00006064461,0.0001264062,0.00005168287],"domain_scores_gemma":[0.9997167,0.0001269004,0.00004326843,0.00002364093,0.00006720329,0.00002224865],"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":[0.00001584328,0.00001286364,0.0001406692,0.00001035379,0.00000724621,0.00001123673,0.000008843856,0.9871021,0.0003822554,0.00214213,0.000203735,0.00996269],"study_design_scores_gemma":[0.00000243097,0.000009901974,0.00004530505,7.686991e-7,0.000001599308,0.000002076406,0.000003443093,0.9988232,0.0002212807,0.0007152092,0.0001734424,0.000001408308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02891252,0.0001240527,0.965011,0.0000963702,0.000021419,0.00003664854,0.00004765319,0.0002447645,0.00550566],"genre_scores_gemma":[0.8873054,0.0001547677,0.1096736,0.00004165694,0.00001917333,0.00009621499,0.0001306132,0.00008525761,0.00249334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01426891,"threshold_uncertainty_score":0.02837169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021183998639876,"score_gpt":0.2006063575979876,"score_spread":0.1903945176115888,"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."}}