{"id":"W4210991577","doi":"10.1109/vppc53923.2021.9699282","title":"Optimal Energy Management of a Dual-motor Electric Vehicle using Dynamic Programming","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Vehicle Power and Propulsion Conference (VPPC)","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Energy management; Computer science; Dynamic programming; Traction motor; Toolbox; Axle; MATLAB; Driving cycle; Automotive engineering; Electric vehicle; State of charge; Benchmark (surveying); Battery electric vehicle; Computation; Battery (electricity); Energy (signal processing); Engineering; Algorithm; Mechanical engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001433287,0.0002999341,0.0004002765,0.0001977854,0.0001391804,0.00009971773,0.0001888541,0.0002207756,0.0001177312],"category_scores_gemma":[0.00001258804,0.0002989388,0.00009923794,0.0008033209,0.0000823591,0.0001884742,0.0001240646,0.0003711323,0.000007352445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007765951,"about_ca_system_score_gemma":0.0000718449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003652742,"about_ca_topic_score_gemma":0.00001038825,"domain_scores_codex":[0.9981976,0.00005149431,0.0004239077,0.0004529201,0.0003037431,0.0005703146],"domain_scores_gemma":[0.9992151,0.00003990455,0.00008531179,0.000370955,0.0001769829,0.0001117319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000619895,0.0001525899,0.0007566197,0.0003295951,0.0002229334,0.0002869712,0.0001346706,0.0008259511,0.5535079,0.001657037,0.00009048499,0.4419732],"study_design_scores_gemma":[0.00133449,0.0003966564,0.002722104,0.0003924199,0.0001623072,0.0001224474,0.0005900527,0.5375526,0.4530199,0.0006147319,0.0022382,0.0008540506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849573,0.003582228,0.01005894,0.000125376,0.0002144672,0.0002215521,0.0000100714,0.0002901382,0.0005399088],"genre_scores_gemma":[0.9909475,0.002350263,0.006240763,0.00002157144,0.00002468576,0.00003911501,0.000009199751,0.00004552667,0.0003213338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5367267,"threshold_uncertainty_score":0.9999463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175358312818805,"score_gpt":0.2308879329657973,"score_spread":0.2191343498376093,"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."}}