{"id":"W3030245181","doi":"10.1016/j.apenergy.2020.115233","title":"Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios","year":2020,"lang":"en","type":"article","venue":"Applied Energy","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Powertrain; Automotive engineering; Driving range; State of charge; Robustness (evolution); Model predictive control; Fuel efficiency; Energy management; Electric vehicle; Engineering; Range (aeronautics); Computer science; Battery (electricity); Control (management); Torque; Energy (signal processing)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00005934156,0.0001752563,0.0003506954,0.0001227694,0.00003106845,0.0000175259,0.0001578713,0.00008386667,0.000007446587],"category_scores_gemma":[0.00004229295,0.0001871616,0.00002719719,0.0003139481,0.00005817011,0.00005262173,0.00005026102,0.0001599307,0.000002887303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002993024,"about_ca_system_score_gemma":0.00001347829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004968771,"about_ca_topic_score_gemma":0.0000614214,"domain_scores_codex":[0.9990789,0.00001343593,0.0002962228,0.0002138921,0.00009774111,0.0002998151],"domain_scores_gemma":[0.9995969,0.0001357368,0.00004933811,0.000134861,0.00001666918,0.00006649366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005053128,0.00006491342,0.01451845,0.0001888707,0.0002203711,0.00006711682,0.000231961,0.1231564,0.7562371,0.02338746,0.001572469,0.08030433],"study_design_scores_gemma":[0.001149617,0.00004822877,0.0120044,0.00003019674,0.00001739643,0.000005385462,0.0000657937,0.787356,0.1982366,0.0003749884,0.0004490324,0.0002624408],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904093,0.0004391413,0.00616745,0.0001410465,0.00002593893,0.00009018117,0.000003212718,0.002150505,0.0005732039],"genre_scores_gemma":[0.9992482,0.0001514528,0.0004164167,0.000085678,0.00002471366,0.00002826883,0.000006094128,0.00003680883,0.000002381408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6641996,"threshold_uncertainty_score":0.7632229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00513958749831634,"score_gpt":0.1744942061384015,"score_spread":0.1693546186400852,"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."}}