{"id":"W4391799569","doi":"10.1016/j.electacta.2024.143953","title":"Online state estimation of Li-ion batteries using continuous-discrete nonlinear Kalman filters based on a nonlinear simplified electrochemical model","year":2024,"lang":"en","type":"article","venue":"Electrochimica Acta","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Kalman filter; Electrochemistry; Extended Kalman filter; Nonlinear model; Control theory (sociology); Ion; Moving horizon estimation; State (computer science); Computer science; Materials science; Electrode; Chemistry; Algorithm; Physics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001287428,0.000427064,0.0004615565,0.0005162851,0.00007423701,0.00007835374,0.0004477307,0.0002059675,0.00002797465],"category_scores_gemma":[0.0001496828,0.0004230595,0.0001449571,0.0006481914,0.0001373664,0.0002806025,0.00008786246,0.0009530286,0.000009645924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003417428,"about_ca_system_score_gemma":0.0001348866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002318254,"about_ca_topic_score_gemma":0.000005331299,"domain_scores_codex":[0.9976442,0.00003489198,0.0005291722,0.0005169915,0.0004531272,0.0008216366],"domain_scores_gemma":[0.9989882,0.000213838,0.00006927034,0.0005512631,0.00007608654,0.0001013081],"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":[0.0001412344,0.00006576143,0.000004028246,0.0002085055,0.00005479698,0.000007838071,0.00004841921,0.2342024,0.7627854,0.00001436726,0.0002365997,0.002230613],"study_design_scores_gemma":[0.0001935391,0.0001824777,0.000004716397,0.0001241808,0.00001781824,0.000007104211,0.000008777241,0.568095,0.4306241,0.0003588485,0.0001617316,0.0002216499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6965461,0.00008950257,0.3009867,0.0005085363,0.00007332288,0.0003484389,0.0001875856,0.001137518,0.0001223127],"genre_scores_gemma":[0.9271285,0.00006507887,0.07198279,0.0001110277,0.00008172906,0.00003746135,0.0003969096,0.0001534797,0.00004303856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3338926,"threshold_uncertainty_score":0.9998221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443580241642662,"score_gpt":0.2791515609177962,"score_spread":0.2647157585013696,"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."}}