- author = "Nikola Andelic and Sandi {Baressi Segota}",
- title = "An Advanced Methodology for Crystal System Detection in Li-Ion Batteries",
- journal = "Electronics",
- year = "2024",
- volume = "13",
- number = "12",
- pages = "article number: 2278",
- month = jun,
- keywords = "genetic algorithms, genetic programming, crystal structure, genetic programming symbolic classifier, Lithium batteries, oversampling techniques, random hyperparameter value search method",
- ISSN = "2079-9292",
-
URL = "
https://www.mdpi.com/2079-9292/13/12/2278",
-
DOI = "
doi:10.3390/electronics13122278",
- size = "32 pages",
- abstract = "Detecting the crystal system of lithium-ion batteries is crucial for optimising their performance and safety. Understanding the arrangement of atoms or ions within the battery electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was used to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0).",
-
notes = "Also known as \cite{electronics13122278} ECML/PKDD2003
Discovery Challenge, Industrial Electronics
Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia",
