{"id":"W2995940256","doi":"10.1016/j.jpowsour.2019.227638","title":"Aging-aware co-optimization of battery size, depth of discharge, and energy management for plug-in hybrid electric vehicles","year":2019,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Central Universities in China; Natural Science Foundation of Chongqing; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Battery (electricity); Depth of discharge; Automotive engineering; Energy consumption; Battery pack; State of charge; Energy management; Fuel efficiency; Sensitivity (control systems); State of health; Engineering; Energy (signal processing); Simulation; Electrical engineering; Power (physics); Electronic engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003609101,0.0006407388,0.0005665009,0.0003124511,0.0002125715,0.0005072506,0.0006766659,0.0003121622,0.0008710042],"category_scores_gemma":[0.0006867285,0.0002077111,0.0002495752,0.000221981,0.0001526459,0.0006999206,0.0003975684,0.0002904409,0.0001430051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000375791,"about_ca_system_score_gemma":0.0005608575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00185389,"about_ca_topic_score_gemma":0.005845384,"domain_scores_codex":[0.9998779,0.00001862859,0.000006988268,0.00003066217,0.00003092441,0.00003488949],"domain_scores_gemma":[0.9997309,0.00008950612,0.00003889279,0.00001796959,0.00009518477,0.00002759422],"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":[0.0005098337,0.000419247,0.006452023,0.000169092,0.0001064371,0.0001407306,0.00007012503,0.8179518,0.03954745,0.001242567,0.001955956,0.1314348],"study_design_scores_gemma":[0.00001291068,0.0001923499,0.002488749,0.000006083707,0.00005100928,0.00004472084,0.00004366897,0.9903293,0.005529069,0.0006590427,0.000634697,0.000008328776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6995441,0.002975131,0.287911,0.0003690219,0.0002199103,0.00006901602,0.0002165247,0.0009398373,0.007755477],"genre_scores_gemma":[0.9968246,0.0000682165,0.002488052,0.00001669691,0.000008954572,0.000006216834,0.00002879491,0.00001646938,0.0005421503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00185389,"threshold_uncertainty_score":0.00368619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006058471884105963,"score_gpt":0.235793241793345,"score_spread":0.229734769909239,"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."}}