{"id":"W2987488488","doi":"10.1016/j.energy.2019.116476","title":"Intelligent energy management system for conventional autonomous vehicles","year":2019,"lang":"en","type":"article","venue":"Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Australian Research Council; Australian Government; Australian Education International, Australian Government","keywords":"Automotive engineering; Energy management; Throttle; Energy consumption; Fuel efficiency; Engineering; Energy management system; Fuzzy logic; Torque; Intelligent control; Control engineering; Energy (signal processing); Computer science; Electrical engineering; Artificial intelligence","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":[0.0001816107,0.0003602504,0.0003552003,0.0004045864,0.000660818,0.0007270988,0.0006928223,0.0003662237,0.00325972],"category_scores_gemma":[0.0002103258,0.0001513987,0.0001699195,0.0002619877,0.0001911542,0.0005034299,0.0003732828,0.0002606743,0.0008055949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000449016,"about_ca_system_score_gemma":0.0005930389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00393906,"about_ca_topic_score_gemma":0.005448528,"domain_scores_codex":[0.999866,0.0000117568,0.000009738081,0.00004615542,0.00004453173,0.00002168123],"domain_scores_gemma":[0.9998662,0.00001559143,0.00001514283,0.000020659,0.00006869122,0.00001370196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001431507,0.0007771942,0.01001383,0.0002949291,0.0001317265,0.0005440945,0.0005012227,0.1948836,0.1220879,0.01643358,0.03534394,0.6175565],"study_design_scores_gemma":[0.00009010489,0.0003234794,0.003668062,0.00001408343,0.00007095966,0.000109145,0.0001003921,0.9587685,0.01640379,0.003046516,0.0173731,0.0000318417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3098564,0.0006936889,0.6247257,0.0006508902,0.0006772674,0.0003433547,0.0005387924,0.01328202,0.04923197],"genre_scores_gemma":[0.975109,0.00008804166,0.01533955,0.00007465574,0.00004233981,0.00008654842,0.0002223906,0.00003651406,0.009001076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00393906,"threshold_uncertainty_score":0.01090485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004363663084260547,"score_gpt":0.1788656526509919,"score_spread":0.1745019895667314,"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."}}