{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003791033,0.0005420818,0.0003920057,0.0005340765,0.0001721118,0.0007083511,0.0005500855,0.0003696168,0.0007456057],"category_scores_gemma":[0.001407025,0.0001516369,0.000263156,0.0003503696,0.0001913477,0.0008014175,0.0005349651,0.000447989,0.0002937199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000342192,"about_ca_system_score_gemma":0.0001928164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354731,"about_ca_topic_score_gemma":0.002287546,"domain_scores_codex":[0.99967,0.00003919889,0.00002616239,0.00006489015,0.0001793788,0.00002026503],"domain_scores_gemma":[0.9995841,0.0001313683,0.00007180711,0.00005022328,0.0001505141,0.00001194717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008745659,0.0003916819,0.03529014,0.000471566,0.0001381425,0.000339884,0.000318763,0.2214317,0.1125301,0.002153569,0.003632774,0.6224272],"study_design_scores_gemma":[0.000009920663,0.000193277,0.01272217,0.00002876849,0.00002704362,0.0001042541,0.0001014387,0.9058511,0.07804544,0.00118106,0.001707324,0.0000282591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5602268,0.0008653149,0.4289429,0.0002546752,0.0001719115,0.000120701,0.000571493,0.002753152,0.006093115],"genre_scores_gemma":[0.9824005,0.0001280763,0.0152298,0.00006333828,0.000009608917,0.00004323166,0.0002548471,0.00002218346,0.00184841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001354731,"threshold_uncertainty_score":0.002693713,"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."}}