Sodium-ion battery cycle life prediction using machine learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{Liu:2024:GEn-CITy,

• author = "Linfeng Liu and Filbert H. Juwono and W. K. Wong and Erick Purwanto and Tingyan Jin and Yuyang Zhen",
• title = "Sodium-ion battery cycle life prediction using machine learning",
• booktitle = "International Conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)",
• year = "2024",
• volume = "2024",
• pages = "151--155",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• DOI = "10.1049/icp.2025.0249",
• abstract = "In this study, we explore the classification and prediction capabilities of three models-Genetic Programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.",
• notes = "Also known as \cite{10915731}",

}

Genetic Programming entries for Linfeng Liu Filbert H Juwono Wei Kitt Wong Erick Purwanto Tingyan Jin Yuyang Zhen

Citations