{"id":"W3197157228","doi":"10.1109/ojvt.2021.3110243","title":"Hardware-in-the-Loop Validation of Different Power Train Topologies’ Models in Electric Vehicles: A Plug-and-Play Capability","year":2021,"lang":"en","type":"article","venue":"IEEE Open Journal of Vehicular Technology","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Modular design; Hardware-in-the-loop simulation; Robustness (evolution); Plug-in; Computer science; Simulation; Virtual prototyping; Propulsion; Control engineering; Automotive engineering; Engineering; Embedded system","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":[0.000484361,0.0005214185,0.0002518414,0.0002347736,0.0002146099,0.0004410612,0.0006108481,0.0003595974,0.00248623],"category_scores_gemma":[0.0008834111,0.00018595,0.0002678743,0.00009830729,0.0003044275,0.000600335,0.0003507163,0.0004155443,0.0003673758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139924,"about_ca_system_score_gemma":0.0004095372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389519,"about_ca_topic_score_gemma":0.001189501,"domain_scores_codex":[0.999755,0.00008403049,0.00001167822,0.00002412264,0.000100592,0.00002463246],"domain_scores_gemma":[0.9996454,0.0001516318,0.00004066543,0.00008023601,0.00006876131,0.00001343192],"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":[0.000280134,0.0002734477,0.003118617,0.0003437324,0.00004735871,0.0002143491,0.0003372654,0.8692709,0.07665567,0.004484301,0.0007484526,0.04422571],"study_design_scores_gemma":[0.00003124445,0.0004752424,0.0008661381,0.00001798362,0.00001334322,0.00008020232,0.00006211727,0.944619,0.05033483,0.000737235,0.002749017,0.00001353862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3428296,0.00009186877,0.6467797,0.00009120227,0.00005407819,0.0001686925,0.0002056889,0.002775767,0.007003403],"genre_scores_gemma":[0.9795375,0.00003776726,0.0187324,0.00001021011,0.00000255295,0.000078397,0.0001118201,0.00007110806,0.001418287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00248623,"threshold_uncertainty_score":0.008317292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567375506023646,"score_gpt":0.2542467048543032,"score_spread":0.2385729497940668,"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."}}