Apribowo, Chico Hermanu Brillianto and Hadi, Sasongko Pramono and Wijaya, Franscisco Danang and Setyonegoro, Mokhammad Isnaeni Bambang and Sarjiya, Sarjiya (2024) Early prediction of battery degradation in grid-scale battery energy storage system using extreme gradient boosting algorithm. RESULTS IN ENGINEERING, 21: 101709. pp. 1-14. ISSN 2590-1230
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Abstract
The growth of battery energy storage systems (BESS) is caused by the variability and intermittent nature of high demand and renewable power generation at the network scale. In the context of BESS, Lithium-ion (Li-ion) battery occupies a crucial position, although it is faced with challenges related to performance battery degradation over time due to electrochemical processes. This battery degradation is a crucial factor to account for, based on its potential to diminish the efficiency and safety of electrical system equipment, thereby contributing to increased system planning costs. This implies that the health of battery needs to be diagnosed, particularly by determining remaining useful life (RUL), to avoid unexpected operational costs and ensure system safety. Therefore, this study aimed to use machine learning models, specifically extreme gradient boosting (XGBoost) algorithm, to estimate RUL, with a focus on the temperature variable, an aspect that had been previously underemphasized. Utilizing XGBoost m
Item Type: | Article |
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Uncontrolled Keywords: | Battery energy storage system; Battery degradation; Remaining useful life; Extreme gradient boosting algorithm; Hyperparameter tuning |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > Electric apparatus and materials. Electric circuits. Electric networks |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Rita Yulianti Yulianti |
Date Deposited: | 01 Nov 2024 06:14 |
Last Modified: | 01 Nov 2024 06:14 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10371 |