{"id":"W2604453058","doi":"10.1002/oca.2320","title":"Control‐relevant parameter estimation application to a model‐based PHEV power management system","year":2017,"lang":"en","type":"article","venue":"Optimal Control Applications and Methods","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Powertrain; Model predictive control; Controller (irrigation); Control engineering; Power management; Computer science; Automotive industry; Control (management); Control theory (sociology); Power (physics); Engineering; Torque; Artificial intelligence","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":[0.0005024401,0.0001985769,0.0002910085,0.0001243686,0.0003580906,0.0001578529,0.0003263942,0.00009665338,0.000002416777],"category_scores_gemma":[0.00002923454,0.0001890183,0.00005915559,0.00009777582,0.00005658375,0.000119814,0.00003674923,0.000130894,0.00002114306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008039121,"about_ca_system_score_gemma":0.000009905984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008457693,"about_ca_topic_score_gemma":8.873054e-7,"domain_scores_codex":[0.9989743,0.00003070707,0.0002707017,0.0003329401,0.0001131007,0.000278295],"domain_scores_gemma":[0.9987904,0.0001307622,0.00009537016,0.0008150643,0.0000653996,0.0001029682],"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":[0.00004517088,0.00002572076,0.00003399932,0.00008926266,0.00007874327,8.193348e-7,0.000009682936,0.3751886,0.003441518,0.05004213,0.00007890191,0.5709655],"study_design_scores_gemma":[0.0009312553,0.00003822024,0.0007824121,0.00001655457,0.000111694,0.000003053948,0.00001614687,0.9924489,0.0009177411,0.0009504686,0.003572356,0.0002112001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002051466,0.0002253259,0.9929529,0.0003727614,0.0000235908,0.001692033,0.0000257797,0.000642349,0.002013756],"genre_scores_gemma":[0.5436264,0.00001174336,0.4539275,0.00006100794,0.00001058434,0.002316023,0.000003171514,0.00001792368,0.00002563688],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6172603,"threshold_uncertainty_score":0.7707942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00930831965815102,"score_gpt":0.2964352401936179,"score_spread":0.2871269205354669,"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."}}