Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

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dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.authoridBicer, Emre/0000-0002-9871-4102
dc.authoridDumen, Sezer/0000-0001-5752-2860
dc.authoridDURU, IREMNUR/0000-0001-5492-803X
dc.contributor.authorOyucu, Saadin
dc.contributor.authorDumen, Sezer
dc.contributor.authorDuru, Iremnur
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorBicer, Emre
dc.date.accessioned2024-10-26T18:08:04Z
dc.date.available2024-10-26T18:08:04Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractLi-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model's utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.
dc.description.sponsorshipEuropean Union's Horizon Europe research and innovation programme; Horizon Europe
dc.description.sponsorshipThe authors also acknowledge Horizon Europe for their support to our research group.
dc.identifier.doi10.3390/sym16040436
dc.identifier.issn2073-8994
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85191553026
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/sym16040436
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29774
dc.identifier.volume16
dc.identifier.wosWOS:001210694200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLi-ion
dc.subjectSoH
dc.subjectmachine learning
dc.subjectdeep learning
dc.titleDischarge Capacity Estimation for Li-Ion Batteries: A Comparative Study
dc.typeArticle

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