{"id":"W4206060187","doi":"10.1063/5.0074496","title":"Braking energy management strategy for electric vehicles based on working condition prediction","year":2022,"lang":"en","type":"article","venue":"AIP Advances","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"European Metrology Programme for Innovation and Research","keywords":"Regenerative brake; Automotive engineering; Torque; Range (aeronautics); Electronic brakeforce distribution; Electric vehicle; Computer science; Power (physics); Energy management; Dynamic braking; Energy (signal processing); Engineering; Retarder; Brake; Braking system; Mathematics","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":[0.00008529947,0.0001123992,0.00009898383,0.0002185112,0.0002459691,0.00002269992,0.0001362549,0.00002572284,0.00001482451],"category_scores_gemma":[0.000004962806,0.0001250323,0.00004053978,0.0003783835,0.00001089427,0.0001023569,0.00001700388,0.0001285165,0.000001173504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000159916,"about_ca_system_score_gemma":0.000007249176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002376734,"about_ca_topic_score_gemma":0.000003038322,"domain_scores_codex":[0.9992537,0.0000146631,0.0001377479,0.0001817447,0.0001617663,0.0002503304],"domain_scores_gemma":[0.9997202,0.00009432916,0.00003882447,0.0001204778,0.0000100562,0.00001616172],"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":[0.00003123891,0.00003052176,0.0005240675,0.00003965275,0.00002519118,0.000006186842,0.000004537388,0.5586751,0.001200563,0.01330474,0.0008467042,0.4253116],"study_design_scores_gemma":[0.0007112498,0.0006464876,0.001784783,0.00003488527,0.00003402848,0.000005100442,0.000118748,0.8844761,0.01820738,0.01457557,0.07912823,0.0002774543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3162922,0.02148655,0.6212983,0.0005663372,0.001514456,0.001580228,0.000107523,0.009204298,0.02795013],"genre_scores_gemma":[0.998284,0.0004371283,0.0005427018,0.0001084997,0.00005880756,0.0004413309,0.00004273958,0.00002493454,0.00005982682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6819919,"threshold_uncertainty_score":0.5098669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121992161247055,"score_gpt":0.2234720276994182,"score_spread":0.2122521060869477,"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."}}