{"id":"W1985139804","doi":"10.1007/s13177-014-0106-z","title":"Blended Power Management Strategy Using Pattern Recognition for a Plug-in Hybrid Electric Vehicle","year":2014,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Plug-in; Automotive engineering; Power management; Hybrid power; Scheme (mathematics); Hybrid vehicle; Fuel efficiency; Control (management); Power (physics); Adaptation (eye); Energy management; Computer science; Electric vehicle; Engineering; Battery (electricity); Dual (grammatical number); Control engineering; Energy (signal processing); 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":[0.0001301334,0.0004303027,0.0004279301,0.0003643454,0.0004270807,0.0006523504,0.0006374403,0.0003841534,0.002275439],"category_scores_gemma":[0.0001838166,0.0001553391,0.000219119,0.0002820231,0.0001482912,0.0004400832,0.0003238373,0.0002441148,0.0004699012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001980048,"about_ca_system_score_gemma":0.000251877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824739,"about_ca_topic_score_gemma":0.002774085,"domain_scores_codex":[0.9998753,0.00001402009,0.00001044768,0.0000338221,0.00004327736,0.00002305738],"domain_scores_gemma":[0.9998994,0.00001263559,0.00001115717,0.00001093541,0.00005582814,0.0000100003],"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":[0.0007994801,0.0007002749,0.004254239,0.0002155014,0.0000973902,0.0006050487,0.0001844387,0.1380658,0.1726119,0.003550823,0.002991215,0.6759238],"study_design_scores_gemma":[0.00003021651,0.0005042945,0.00254286,0.00001023226,0.00005496119,0.0002114134,0.00008580172,0.9502079,0.04186781,0.001297596,0.003167784,0.00001914271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2580403,0.000273152,0.7232358,0.0002771,0.0001852282,0.0001537886,0.00007032654,0.001662603,0.01610162],"genre_scores_gemma":[0.9755455,0.00004781464,0.02032274,0.00003880695,0.00001191793,0.00002941054,0.00003318917,0.00001420967,0.003956583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002275439,"threshold_uncertainty_score":0.007612109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07002551965125416,"score_gpt":0.3375733859304179,"score_spread":0.2675478662791638,"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."}}