{"id":"W2962736777","doi":"10.1049/joe.2018.9234","title":"State‐of‐Charge estimation of Li‐ion battery at different temperatures using particle filter","year":2019,"lang":"en","type":"article","venue":"The Journal of Engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Battery (electricity); State of charge; Particle filter; Residual; Computer science; Heuristic; Transient (computer programming); Algorithm; Filter (signal processing); Control theory (sociology); Computation; Power (physics); Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001781216,0.0001089583,0.0002186958,0.0001227725,0.0000156369,0.000007554033,0.0002143562,0.00003327574,0.00004416278],"category_scores_gemma":[0.00005241803,0.00007482944,0.00005343548,0.0001271932,0.00002061981,0.0001762024,0.00007438542,0.0002603755,0.000004175494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001022074,"about_ca_system_score_gemma":0.000004613902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.743201e-7,"about_ca_topic_score_gemma":2.723767e-7,"domain_scores_codex":[0.9991689,0.00001646511,0.0003545145,0.00004428585,0.0002276031,0.0001882633],"domain_scores_gemma":[0.9994675,0.0001483193,0.0001009566,0.0002055073,0.00004794902,0.00002979524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001305536,0.000005213619,0.0005162798,0.00008126752,0.0000231013,0.000001094393,0.00007610811,0.5219661,0.4768544,0.000004117731,0.00001442275,0.0004448165],"study_design_scores_gemma":[0.0001369745,0.00004800694,0.002162072,0.0001258534,0.000008187295,0.00002374098,0.00001525364,0.4222153,0.5751829,0.00001828263,0.0000129346,0.00005051407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692435,0.0004304654,0.02996475,0.00002492899,0.000205029,0.00008491002,0.000003981774,0.00003543217,0.000007013994],"genre_scores_gemma":[0.998749,0.00009434365,0.001086184,0.000002766158,0.00002315594,7.611184e-7,5.46039e-7,0.000025175,0.00001808962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09975088,"threshold_uncertainty_score":0.3051456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442197352357398,"score_gpt":0.2477398145674239,"score_spread":0.2333178410438499,"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."}}