{"id":"W3036507558","doi":"10.1109/tvt.2020.3004010","title":"Sizing of a Battery Pack Based on Series/Parallel Configurations for a High-Power Electric Vehicle as a Constrained Optimization Problem","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"European Regional Development Fund; Fundação para a Ciência e a Tecnologia; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Battery pack; Sizing; Battery (electricity); Electric vehicle; Electric-vehicle battery; Mathematical optimization; Series (stratigraphy); Exploit; Computer science; Constraint (computer-aided design); Power (physics); Pareto principle; Engineering; Automotive engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003687418,0.0009728929,0.0006258663,0.0006227763,0.0003487763,0.0009800633,0.000661212,0.0006645743,0.004266287],"category_scores_gemma":[0.0007201954,0.0005376004,0.0007936119,0.0007593127,0.0004250604,0.0008845418,0.0005448813,0.0005346261,0.000552787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005766621,"about_ca_system_score_gemma":0.0008584752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002168295,"about_ca_topic_score_gemma":0.003817626,"domain_scores_codex":[0.999732,0.00008112468,0.00001213599,0.00004225939,0.000103202,0.00002928252],"domain_scores_gemma":[0.9997593,0.0001137523,0.00003552391,0.00002399595,0.00005356349,0.00001400382],"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":[0.00003372451,0.00002873469,0.000362876,0.0001100977,0.00002954876,0.0001145675,0.00003093413,0.9632891,0.005208352,0.003158667,0.0006676706,0.02696559],"study_design_scores_gemma":[0.00001216307,0.0001224581,0.0002659764,0.00001406693,0.00003110085,0.00007931086,0.00004605178,0.9909116,0.003034652,0.003707964,0.001765831,0.000008907662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09439687,0.0003311287,0.8813319,0.0001655461,0.00004357399,0.0002227885,0.0001968931,0.0004615455,0.02284971],"genre_scores_gemma":[0.7650871,0.0003373884,0.22525,0.00007447789,0.00002223532,0.0003546068,0.0002916183,0.0001715428,0.008410987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004266287,"threshold_uncertainty_score":0.01427215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063879850555345,"score_gpt":0.2294545252479263,"score_spread":0.2188157267423728,"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."}}