{"id":"W2013005892","doi":"10.1504/ijehv.2014.062806","title":"Intelligent power management of plug-in hybrid electric vehicles, part II: real-time route based power management","year":2014,"lang":"en","type":"article","venue":"International Journal of Electric and Hybrid Vehicles","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Power management; Plug-in; Power management system; Automotive industry; Automotive engineering; Controller (irrigation); Control engineering; Fuel efficiency; Energy management; Hybrid vehicle; Engineering; Computer science; Power (physics); Operating system","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":[0.0001182361,0.000353702,0.0002144912,0.0001855766,0.0001340244,0.0004928628,0.0004294702,0.0001758634,0.0010473],"category_scores_gemma":[0.0001685902,0.0001306048,0.00015236,0.0001224224,0.0001694289,0.000340616,0.0002184073,0.0002277072,0.0002130236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001480086,"about_ca_system_score_gemma":0.0001311864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009615481,"about_ca_topic_score_gemma":0.001327243,"domain_scores_codex":[0.9999329,0.00001262351,0.000004613832,0.0000190381,0.00002348406,0.000007285686],"domain_scores_gemma":[0.9999399,0.0000161861,0.00001370595,0.00001085889,0.00001626775,0.000003083184],"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":[0.0002488521,0.0001627875,0.002930083,0.0003064766,0.0001156372,0.0002719036,0.0001676407,0.4522977,0.1144508,0.00693725,0.002540865,0.4195701],"study_design_scores_gemma":[0.00003189963,0.000211833,0.00228328,0.00001024065,0.00003437345,0.0001256628,0.00004712635,0.955229,0.03072983,0.002306376,0.008976323,0.00001398163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1466672,0.0005017017,0.8404493,0.0001071473,0.00007052254,0.0000897839,0.00007017428,0.001671377,0.01037294],"genre_scores_gemma":[0.9813712,0.0001171217,0.01566936,0.00001674428,0.00001065288,0.00002916567,0.00005057092,0.00002673739,0.002708432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0010473,"threshold_uncertainty_score":0.003503621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006087867255621006,"score_gpt":0.2123017554414018,"score_spread":0.2062138881857808,"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."}}