{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003080185,0.0003031458,0.0003463071,0.0006332841,0.00006278793,0.00007451652,0.0002536671,0.0001211055,0.000003389423],"category_scores_gemma":[0.0003290798,0.0003095942,0.00004997155,0.0005853011,0.00003574615,0.0003399582,0.0001064237,0.0003488302,0.000008104147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000363175,"about_ca_system_score_gemma":0.00002441829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001211861,"about_ca_topic_score_gemma":0.00002962111,"domain_scores_codex":[0.9982701,0.00001072948,0.0004804132,0.0003595066,0.0001921375,0.0006871485],"domain_scores_gemma":[0.998872,0.0005368495,0.00004155073,0.0004493102,0.00004974866,0.00005051127],"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":[0.0001538461,0.000009741525,0.0001245865,0.0004802259,0.00004594998,0.00002852188,0.0001148405,0.9668902,0.02049551,0.00006002682,0.0001965464,0.01140002],"study_design_scores_gemma":[0.002454266,0.00005840518,0.001314848,0.000783944,0.000008740825,0.000006755484,0.0000361653,0.969613,0.02442004,0.0002187089,0.0007403567,0.0003448261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.258168,0.00009853951,0.7375458,0.0001363195,0.0002014707,0.0008150702,0.00006876649,0.00158931,0.001376724],"genre_scores_gemma":[0.9498847,0.0001432177,0.04914746,0.000008715103,0.00003847373,0.0003123369,0.00007099374,0.0001030406,0.0002910307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6917167,"threshold_uncertainty_score":0.9999356,"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."}}