{"id":"W2751550173","doi":"10.1088/1757-899x/235/1/012013","title":"Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm","year":2017,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Guangdong Province","keywords":"Powertrain; Genetic algorithm; Electric vehicle; Fuel efficiency; Convergence (economics); Hybrid power; Component (thermodynamics); Automotive engineering; Computer science; Optimization algorithm; Hybrid algorithm (constraint satisfaction); Power (physics); Series (stratigraphy); Algorithm; Mathematical optimization; Engineering; Mathematics; Torque","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":[0.0003100463,0.0005448317,0.0005792486,0.0004692162,0.0003308311,0.0005962694,0.0005076429,0.0005387623,0.001245545],"category_scores_gemma":[0.0005290351,0.0002211695,0.0004178432,0.0006070716,0.0003286485,0.000287893,0.0003209127,0.0003526616,0.0001597359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005710756,"about_ca_system_score_gemma":0.0007405942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007812828,"about_ca_topic_score_gemma":0.005880656,"domain_scores_codex":[0.9998599,0.00004735401,0.000004050791,0.00002393584,0.00004876958,0.00001592645],"domain_scores_gemma":[0.999891,0.00005910732,0.00001310696,0.000004602507,0.00002716794,0.000004908253],"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":[0.000008752293,0.000008985279,0.0001305871,0.00001018375,0.00001115356,0.00001048524,0.000008422274,0.9880233,0.0003230924,0.001579976,0.0001718364,0.009713227],"study_design_scores_gemma":[0.000003535982,0.000009147617,0.00005313301,0.000001489217,0.000002585332,0.000002943641,0.00000231271,0.9990747,0.00009438106,0.0005838328,0.000170582,0.000001256501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05266533,0.0004549046,0.9330909,0.0001747114,0.00005772948,0.00007979793,0.00004559458,0.0003805803,0.0130505],"genre_scores_gemma":[0.8441161,0.0004596871,0.1473133,0.00007670357,0.00003403285,0.0003197409,0.0001047444,0.00006024473,0.007515399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007812828,"threshold_uncertainty_score":0.0155347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007099894970205506,"score_gpt":0.1857573314558325,"score_spread":0.178657436485627,"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."}}