{"id":"W1991268814","doi":"10.1177/0954407014547925","title":"Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions","year":2014,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":13,"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; Automotive engineering; Controller (irrigation); Plug-in; Battery (electricity); Electric vehicle; Engineering; Torque; Power (physics); Control engineering; Computer science; Control (management)","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":[0.003132426,0.00015022,0.0004104716,0.0001901126,0.00004247171,0.00001520678,0.0002842693,0.00007086611,4.258061e-7],"category_scores_gemma":[0.001091693,0.00009006183,0.00009855939,0.000287799,0.000064743,0.0001473036,0.00003387207,0.0002783929,4.388269e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001076655,"about_ca_system_score_gemma":0.00005544226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001987776,"about_ca_topic_score_gemma":1.177944e-7,"domain_scores_codex":[0.9988192,0.00002092561,0.0006151059,0.0001093548,0.000244961,0.000190441],"domain_scores_gemma":[0.9988132,0.0004607414,0.0002508709,0.00009232136,0.0003291662,0.00005368133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002078159,0.0000241272,0.00001674984,0.0005075796,0.0001696494,1.607674e-7,0.0001895528,0.8949072,0.07715288,0.02371768,0.00004627706,0.003060383],"study_design_scores_gemma":[0.001996518,0.000268082,0.0000794367,0.0003302476,0.0001515686,0.00003106214,0.00009819326,0.8531935,0.1429676,0.000760789,0.00004716778,0.00007583884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6260565,0.001266352,0.37046,0.0003122977,0.0002427097,0.00153849,0.000006282136,0.00006954104,0.00004780527],"genre_scores_gemma":[0.9985981,0.00005648189,0.001166309,0.000006200093,0.00004504373,0.0001117819,9.687545e-8,0.00001506462,9.42905e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3725416,"threshold_uncertainty_score":0.3672615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006408321431024763,"score_gpt":0.1982677789887274,"score_spread":0.1918594575577026,"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."}}