Ensemble Learning in Li-Ion Battery Management Systems: Focus on Voting Regression for Capacity Estimation

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dc.contributor.authorAsal, Burcak
dc.contributor.authorOyucu, Saadin
dc.contributor.authorAksoz, Ahmet
dc.date.accessioned2025-05-04T16:41:58Z
dc.date.available2025-05-04T16:41:58Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractAccurate estimation of discharge capacity in lithium-ion batteries is very important for optimizing their performance and longevity and directly affects the efficiency of battery management systems (BMS). Traditional models frequently encounter challenges in handling the non-linearities and complex interdependencies inherent in battery behavior. This study introduces a robust voting regression-based approach that combines multiple regression models to improve prediction accuracy and reliability. We employ four different regression configurations, Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Regression and K-Nearest Neighbor (KNN) to form a combined estimator through voting mechanisms. The robustness of our voting-based approach is further validated through extensive experimentation on two real-world battery datasets including various operational conditions. Our results show that the voting regression approach we propose provides stable and accurate performance, making it a viable tool for different BMS applications. We also discuss the potential practical implications of our proposed voting approach and suggest directions for future research to further refine and adapt this approach to different types of battery technologies and configurations. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710999
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207929078
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710999
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35018
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250504
dc.subjectbattery management systems
dc.subjectcapacity prediction
dc.subjectensemble approaches
dc.subjectmachine learning
dc.subjectvoting regression
dc.titleEnsemble Learning in Li-Ion Battery Management Systems: Focus on Voting Regression for Capacity Estimation
dc.typeConference Object

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