{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002052776,0.0003564636,0.000424344,0.0002933686,0.0002259765,0.0005413205,0.0003951128,0.0005488793,0.0005813653],"category_scores_gemma":[0.0006548757,0.0001886274,0.0003584459,0.0002788364,0.0001474894,0.0004591801,0.0001968217,0.0003558958,0.0001348036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003286635,"about_ca_system_score_gemma":0.0003617754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008487923,"about_ca_topic_score_gemma":0.005980046,"domain_scores_codex":[0.9999317,0.00001117203,0.000004501178,0.00001928351,0.00002309189,0.00001024922],"domain_scores_gemma":[0.9998503,0.00005862873,0.00002064492,0.00001118993,0.00005386908,0.000005342059],"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":[0.0001650142,0.0000773353,0.005562214,0.0001121578,0.0000678059,0.00009332004,0.0001016717,0.9152748,0.01240627,0.001182904,0.0008355467,0.06412087],"study_design_scores_gemma":[0.000003248337,0.00001267468,0.0009120918,0.00000203875,0.000005148443,0.000006129012,0.000004482183,0.9972979,0.001494883,0.0001541631,0.0001035972,0.000003581765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2560428,0.0004445795,0.7380459,0.0001795071,0.00006834255,0.00004690955,0.0001384739,0.0008066571,0.004226845],"genre_scores_gemma":[0.9782325,0.0001682418,0.0203702,0.00002098154,0.000009981995,0.00003683951,0.0001021614,0.00001427184,0.001044702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008487923,"threshold_uncertainty_score":0.016877,"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."}}