{"id":"W3009193590","doi":"10.1016/j.energy.2020.117297","title":"Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle","year":2020,"lang":"en","type":"article","venue":"Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":348,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Energy management; Computer science; Artificial intelligence; Electric vehicle; Process (computing); Deep learning; Battery (electricity); Brake; Industrial engineering; Mathematical optimization; Automotive engineering; Energy (signal processing); Power (physics); Engineering; Mathematics","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":[0.0003605523,0.0005495111,0.0006534409,0.0002519697,0.0003096348,0.00055704,0.00099696,0.0006414452,0.002919815],"category_scores_gemma":[0.0006566237,0.0002274566,0.0002651663,0.0001859984,0.0003396816,0.0005033389,0.0007021487,0.0006796541,0.0002899313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000524241,"about_ca_system_score_gemma":0.000828209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007994564,"about_ca_topic_score_gemma":0.009275477,"domain_scores_codex":[0.9998403,0.00002065972,0.00000877705,0.00004302261,0.00003823442,0.00004895959],"domain_scores_gemma":[0.9997664,0.00006870579,0.00002894441,0.00001701157,0.00008976371,0.0000291722],"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":[0.0001367099,0.0001242052,0.001060941,0.00004002297,0.00003029583,0.0001115348,0.00004222252,0.9248688,0.003275714,0.003087843,0.001430236,0.0657915],"study_design_scores_gemma":[0.000005774764,0.00002259323,0.00007990145,0.000002230446,0.000003884521,0.000007683501,0.000003842239,0.9988436,0.0003035618,0.0006296047,0.00009533129,0.000002075953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1654277,0.0005171987,0.8168576,0.0004253813,0.0001447114,0.0001066011,0.0001141118,0.0009273405,0.01547927],"genre_scores_gemma":[0.9887553,0.0000340872,0.009451446,0.00006817958,0.000008569583,0.00002782812,0.00003135407,0.00001116463,0.001612085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007994564,"threshold_uncertainty_score":0.01589608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005796716055064546,"score_gpt":0.189519297091785,"score_spread":0.1837225810367205,"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."}}