{"id":"W3206904992","doi":"10.3390/asi4040078","title":"Soft Sensors for State of Charge, State of Energy, and Power Loss in Formula Student Electric Vehicle","year":2021,"lang":"en","type":"article","venue":"Applied System Innovation","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mean squared error; State of charge; Autoregressive model; Electric vehicle; Artificial neural network; Parametric statistics; Control theory (sociology); Power (physics); Battery (electricity); Computer science; Engineering; Simulation; Mathematics; Statistics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002300523,0.0001011879,0.0002371988,0.0003563728,0.00001609479,0.00000836159,0.0000855058,0.00004887488,0.000001381466],"category_scores_gemma":[0.00002446426,0.0001070785,0.00001285487,0.001343994,0.00001909549,0.00006638063,0.00004772532,0.00007992404,7.035035e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001237918,"about_ca_system_score_gemma":0.00001983139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003102268,"about_ca_topic_score_gemma":0.00000707253,"domain_scores_codex":[0.9989331,0.000009544146,0.0004916117,0.0001569965,0.0001880227,0.0002207187],"domain_scores_gemma":[0.999418,0.00007045559,0.0001037933,0.0001895905,0.0002074608,0.00001071366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004599774,0.0000394575,0.001417449,0.0007468773,0.00005303837,0.000005222373,0.0002381564,0.01353984,0.9197859,0.02185247,0.00003207677,0.04224351],"study_design_scores_gemma":[0.0007400794,0.00004895736,0.003789006,0.00005998699,0.000002667536,0.000002597307,0.0001891867,0.01460264,0.9790199,0.001230841,0.0001835225,0.000130598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556942,0.000102543,0.04348192,0.00001359999,0.00003172929,0.0003185162,0.00002159679,0.00008955112,0.000246346],"genre_scores_gemma":[0.999367,0.00002753035,0.0003989076,0.000005698057,0.000004313103,0.0001126556,0.0000192913,0.00002530515,0.00003929881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05923402,"threshold_uncertainty_score":0.4366535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033618467013403,"score_gpt":0.255032869285893,"score_spread":0.2446966846157589,"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."}}