{"id":"W4412736122","doi":"10.1016/j.rineng.2025.106428","title":"XGBoost–random forest stacking with dual-state Kalman filtering for real-time battery SOC estimation","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Botswana International University of Science and Technology","keywords":"Kalman filter; Dual (grammatical number); Stacking; Computer science; Random forest; Estimation; Extended Kalman filter; Moving horizon estimation; Battery (electricity); State (computer science); Artificial intelligence; Algorithm; Engineering; Chemistry; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089735,0.001244297,0.0009878977,0.0005901525,0.0004069514,0.0005647762,0.001052625,0.0007986591,0.002410435],"category_scores_gemma":[0.001822331,0.0005156629,0.0007303683,0.0005981897,0.0002715818,0.0007474109,0.0004972036,0.001016084,0.001314453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004216536,"about_ca_system_score_gemma":0.001243629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01270249,"about_ca_topic_score_gemma":0.01789279,"domain_scores_codex":[0.9996206,0.00006692579,0.00001857369,0.0001168644,0.00009505454,0.00008196144],"domain_scores_gemma":[0.9995263,0.0001428039,0.00005659529,0.00006026146,0.000192442,0.00002170874],"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":[0.0002877745,0.0001668972,0.002940163,0.0001014644,0.0001587551,0.00006566496,0.0000754474,0.5130329,0.007993686,0.001949127,0.006430345,0.4667978],"study_design_scores_gemma":[0.00000836575,0.0000217244,0.0003825817,0.000004495277,0.000009306377,0.000007970953,0.000006520822,0.9971313,0.001128247,0.000657643,0.0006354623,0.000006355514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02535023,0.0003643525,0.9676089,0.000100467,0.00008790891,0.00004821896,0.0001285355,0.005107535,0.001203895],"genre_scores_gemma":[0.5640116,0.0002067952,0.428709,0.0002286226,0.0001030846,0.0001702503,0.0009684898,0.0004079684,0.005194157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01270249,"threshold_uncertainty_score":0.02525711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008479900682484843,"score_gpt":0.2510428468649204,"score_spread":0.2425629461824356,"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."}}