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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Zraibi, Brahim
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Improving lithium-ion battery reliability through neural network remaining useful life prediction Zraibi, Brahim; Mansouri, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp83-91

Abstract

The reliable performance of lithium-ion batteries is crucial for the safe and efficient operation of electrical systems, particularly in electric vehicles. To mitigate the risk of battery failure due to degradation, accurate forecasting of the remaining useful life (RUL) is imperative. In this study, we propose employing various recurrent neural network (RNN) methods, including RNN, gated recurrent unit (GRU), and long short-term memory (LSTM), to enhance RUL prediction accuracy for lithium-ion batteries. Our approach aims to provide reliable, accurate, and simple estimates of remaining battery life, facilitating effective management of electric vehicle power systems and minimizing the risk of failure. Performance evaluation metrics such as mean absolute error (MAE), R-squared (R²), mean absolute percentage error (MAPE), and root mean squared error (RMSE) are utilized to assess prediction accuracy. Experimental validation conducted using the NASA lithium-ion battery dataset demonstrates the superiority of LSTM in reducing prediction error and enhancing RUL prediction performance compared to alternative approaches. These findings underscore the potential of neural network methodologies in advancing battery management practices and ensuring the longevity and reliability of lithium-ion battery systems.
Practical application of lithium-ion battery management systems: heating system Zraibi, Brahim; Mansouri, Mohamed; Ezzahi, Abdelghani
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp1389-1398

Abstract

This paper presents a lithium-ion battery management system (BMS) aimed at improving battery longevity through hardware and software optimization. The system targets enhancing energy efficiency in heating devices like burners, commonly used in industrial and domestic applications. A key innovation is the modification of the Arduino Pro Mini 8 MHz 3.3 V microcontroller to reduce power consumption during sleep mode. The study evaluates two iterations of the system: an initial manually soldered prototype using the Arduino board and a second iteration with a robust printed circuit board assembly (PCBA). The transition to the PCBA improved system efficiency and eliminated connection issues. The development integrates conventional circuitry and modern software strategies for efficient battery charge/discharge management. Results from both prototypes demonstrate significant improvements in battery life, offering a sustainable solution for energy-efficient applications.
Co-Authors Ezzahi, Abdelghani Mansouri, Mohamed