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Sucheera Phramala. Comparative analysis of algorithms for predicting the remaining useful life of lithium-ion batteries. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
Comparative analysis of algorithms for predicting the remaining useful life of lithium-ion batteries
Name: Weergul Pratumgul
Organization :
Pathumwan Institute of Technology. Faculty of Engineering
Email :
weeragul@hotmail.com
Name: Waratchaya Pratumgul
Organization :
Pathumwan Institute of Technology. Faculty of Engineering
Email :
waratchaya.btie@gmail.com
Name: Sucheera Phramala
Organization :
Pathumwan Institute of Technology. Faculty of Engineering
Email :
sucheera.tae@gmail.com
LCSH:
Lithium ion batteries
LCSH:
Predictive analytics
LCSH:
Machine learning
LCSH:
Predictive analytics
Abstract:
Lithium-ion batteries are widely used in portable devices, electric vehicles, and large-scale energy storage systems. Predicting remaining useful life (RUL) of these batteries is crucial for ensuring the reliability and safe operation of battery-powered systems. This study aims to identify the most effective method for prediction RUL by evaluating predictions derived form the CALCE dataset. We compare the performance of Bi-LSTM, a deep learning algorithm, with model-based approaches (Kalman filter) and data-driven methods (Support Vector Machine: SVM), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM). Data processing will vary based on the type of non-linearity being analyzed. Model performance will be assessed using validation metrics such as RMSE, MAE, and relative error. The evaluation indicates that while other models enhance the temporal dependence of battery data, Bi-LSTM consistently outperforms them in accuracy and in capturing the temporal and nonlinear relationships within the data. This research is significant for identifying effective machine learning strategies to enhance the performance and reliability of lithium-ion battery management systems (IBMS).
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2025-06-24
Issued:
2025-06-24
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and King Mongkut's University of Technology North Bangkok. Department of Electrical and Computer Engineering. 13th International Electrical Engineering Congress (iEECON 2025) (P06414). Bangkok : Electrical Engineering Academic Association (Thailand), 2025
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.033961 วินาที
Weergul Pratumgul
| Title | Contributor | Type |
|---|---|---|
| Comparative analysis of algorithms for predicting the remaining useful life of lithium-ion batteries
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Weergul Pratumgul;Waratchaya Pratumgul;Sucheera Phramala | บทความ/Article |
Waratchaya Pratumgul
| Title | Contributor | Type |
|---|---|---|
| Comparative analysis of algorithms for predicting the remaining useful life of lithium-ion batteries
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Weergul Pratumgul;Waratchaya Pratumgul;Sucheera Phramala | บทความ/Article |
Sucheera Phramala
| Title | Contributor | Type |
|---|---|---|
| Improving the efficiency of short-term load forecasting using fuzzy optimization combined with load feature recognition
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Kittipong Sriamad;Weeragul Pratumgul;Sucheera Phramala | บทความ/Article | |
| Comparative analysis of algorithms for predicting the remaining useful life of lithium-ion batteries
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Weergul Pratumgul;Waratchaya Pratumgul;Sucheera Phramala | บทความ/Article |