{"id":"W3036425958","doi":"10.1155/2020/8813137","title":"Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users’ Driving and Charging Behavior","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Driving range; TRIPS architecture; Range (aeronautics); Simulation; Battery (electricity); Battery capacity; Electric vehicle; Transport engineering; Computer science; Automotive engineering; Travel behavior; Engineering; Power (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008861154,0.0001478641,0.0002242333,0.0001320049,0.00006805381,0.00002823051,0.0000725569,0.0000619731,0.000004867051],"category_scores_gemma":[0.00001691244,0.0001455054,0.0000962055,0.0001741742,0.00000511804,0.0003968256,7.456837e-7,0.0002707615,1.869196e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000427866,"about_ca_system_score_gemma":0.00001881767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.034691e-7,"about_ca_topic_score_gemma":0.000001588929,"domain_scores_codex":[0.999111,0.000008228632,0.0003828565,0.0001215335,0.0001754368,0.0002009251],"domain_scores_gemma":[0.99959,0.00005919503,0.0001166985,0.00004595582,0.00008069685,0.0001074732],"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":[0.00006362111,0.000008251291,0.004095763,0.00006417009,0.00001281146,0.000009758375,0.0004631097,0.8968033,0.08994649,0.000007432565,0.000009921404,0.008515374],"study_design_scores_gemma":[0.001303051,0.0003243154,0.04547726,0.0001263885,0.00009459924,0.000005473966,0.0001176393,0.9437703,0.008528624,0.00003013122,0.00004357429,0.0001785707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7683058,0.0002087588,0.2310932,0.0001393465,0.00007508518,0.000133036,0.000003630972,0.00003777465,0.000003361236],"genre_scores_gemma":[0.9785061,0.0001125423,0.02102856,0.0001182826,0.0001767842,0.000008799601,0.000008071519,0.00004033513,5.434925e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2102003,"threshold_uncertainty_score":0.5933539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008616701597340305,"score_gpt":0.2152071456546311,"score_spread":0.2065904440572908,"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."}}