{"id":"W4293161848","doi":"10.1007/978-3-031-01503-8","title":"Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles","year":2019,"lang":"en","type":"book","venue":"Synthesis lectures on advances in automotive technology","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Electrification; Powertrain; Reinforcement learning; Diversification (marketing strategy); Energy management; Automotive engineering; Engineering; Decarburization; Computer science; Electricity; Energy (signal processing); Business; Artificial intelligence; Electrical engineering; Materials science","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":[0.0001078656,0.0004873033,0.0003562983,0.0001292489,0.0001093081,0.0004611821,0.0004210499,0.0003034735,0.004613981],"category_scores_gemma":[0.0001665775,0.0001366594,0.0002279683,0.0001671488,0.0002230471,0.0003504526,0.0003794587,0.0007837304,0.001110971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003372974,"about_ca_system_score_gemma":0.0001881609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001248875,"about_ca_topic_score_gemma":0.001259697,"domain_scores_codex":[0.9999516,0.000006154494,0.000001822594,0.00001044595,0.0000228393,0.000007126875],"domain_scores_gemma":[0.9999604,0.00001524489,0.000003655272,0.000004235929,0.00001175128,0.000004644722],"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":[0.0001309034,0.0001009602,0.0002394016,0.0001951976,0.00005121058,0.00007469807,0.00004047452,0.512078,0.01920788,0.03474207,0.01844673,0.4146924],"study_design_scores_gemma":[0.00001556176,0.00008086776,0.0004353331,0.00003748862,0.00001099877,0.00005011458,0.0000120407,0.9390994,0.004493697,0.02324131,0.03251114,0.0000119542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02250871,0.01229948,0.8487615,0.0009742946,0.001141914,0.00004234797,0.0001288953,0.001720078,0.1124228],"genre_scores_gemma":[0.7777196,0.005755539,0.07431541,0.0002046108,0.0004120796,0.00006621376,0.000209375,0.0002279558,0.1410892],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004613981,"threshold_uncertainty_score":0.01543528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005039064363084049,"score_gpt":0.2157217341676091,"score_spread":0.2106826698045251,"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."}}