{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001384042,0.0002269776,0.0001987398,0.000547026,0.0003746859,0.0000322003,0.000159932,0.00006594341,0.00002385419],"category_scores_gemma":[7.854517e-7,0.0002753609,0.00007890734,0.0006987806,0.00002638863,0.0001292924,5.402664e-8,0.0003552894,0.000002428753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002214779,"about_ca_system_score_gemma":0.0000330477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002053343,"about_ca_topic_score_gemma":0.000006672782,"domain_scores_codex":[0.9987267,0.00002901184,0.0003654803,0.0003452814,0.0001979939,0.0003355707],"domain_scores_gemma":[0.9995546,0.00007996771,0.00007655273,0.0001850618,0.00005130165,0.00005256636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003742209,0.0003684724,0.00005672622,0.0003393686,0.0002446426,0.00001813474,0.0004754746,0.3978197,0.3996612,0.003260842,0.0007882641,0.196593],"study_design_scores_gemma":[0.001068247,0.0003926154,0.0007480264,0.000008861518,0.0001504645,0.0000100418,0.0001980793,0.167183,0.8246995,0.0007668671,0.004378569,0.000395761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1870622,0.001155438,0.8096681,0.00008344175,0.0001432301,0.0004073214,0.0001032249,0.001103014,0.0002740196],"genre_scores_gemma":[0.99709,0.000793299,0.0007067791,0.00006861161,0.00001220346,0.00058336,0.0001934838,0.00005763787,0.0004946219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8100278,"threshold_uncertainty_score":0.9999698,"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."}}