Version 1
: Received: 30 August 2024 / Approved: 30 August 2024 / Online: 30 August 2024 (09:39:00 CEST)
How to cite:
Liang, Y.; Zhao, S. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach. Preprints2024, 2024082207. https://doi.org/10.20944/preprints202408.2207.v1
Liang, Y.; Zhao, S. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach. Preprints 2024, 2024082207. https://doi.org/10.20944/preprints202408.2207.v1
Liang, Y.; Zhao, S. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach. Preprints2024, 2024082207. https://doi.org/10.20944/preprints202408.2207.v1
APA Style
Liang, Y., & Zhao, S. (2024). Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach. Preprints. https://doi.org/10.20944/preprints202408.2207.v1
Chicago/Turabian Style
Liang, Y. and Shuai Zhao. 2024 "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach" Preprints. https://doi.org/10.20944/preprints202408.2207.v1
Abstract
In the realm of Lithium-ion batteries, issues like material aging and capacity decline contribute to performance degradation or potential safety hazards. Predicting Remaining Useful Life (RUL) serves as a crucial method to assess the health of batteries, thereby enhancing reliability and safety. Currently hybrid approaches for batteries RUL estimation are prevalence and gained fruitful development. In this paper, a hybrid voting ensembles combining Gradient Boosting, Random Forest and K-Nearest Neighbors is proposed to predict the capacity fade trend and knee point. Finally, extensive experiments are conducted using CALCE CS2 datasets. According to the experimental results, the proposed approach supersedes the single deep learning approach for RUL prediction and the knee point is predicted accurately. Besides prediction purpose, this innovated approach can be also proposed to be integrated into the real-world application for wider usage.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
