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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Mossaddek, Meriem
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries Mossaddek, Meriem; Laadissi, El Mehdi; Ennawaoui, Chouaib; Bouzaid, Sohaib; Hajjaji, Abdelowahed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2449-2456

Abstract

Precisely characterizing Li-ion batteries is essential for optimizing their performance, enhancing safety, and prolonging their lifespan across various applications, such as electric vehicles and renewable energy systems. This article introduces an innovative nonlinear methodology for system identification of a Li-ion battery, employing a nonlinear autoregressive with exogenous inputs (NARX) model. The proposed approach integrates the benefits of nonlinear modeling with the adaptability of the NARX structure, facilitating a more comprehensive representation of the intricate electrochemical processes within the battery. Experimental data collected from a Li-ion battery operating under diverse scenarios are employed to validate the effectiveness of the proposed methodology. The identified NARX model exhibits superior accuracy in predicting the battery's behavior compared to traditional linear models. This study underscores the importance of accounting for nonlinearities in battery modeling, providing insights into the intricate relationships between state-of-charge, voltage, and current under dynamic conditions.
Efficient SOC estimation for electric vehicles: Extended Kalman filter approach for lithium-ion battery systems Mossaddek, Meriem; Laadissi, El Mehdi; Bouzaid, Sohaib; Hajjaji, Abdelowahed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp440-447

Abstract

This study investigates the estimation of the state of charge (SOC) in lithium-ion batteries by utilizing the extended Kalman filter (EKF) algorithm. A simulation model was developed in MATLAB, integrating the Thevenin model with the EKF algorithm to assess SOC levels. The results from the simulations confirm the accuracy and reliability of the proposed approach in estimating SOC. Moreover, a Simulink-based model of the Thevenin equivalent circuit and the EKF algorithm was implemented to further verify the effectiveness of the EKF in SOC estimation. This research underscores the potential of the EKF algorithm to deliver precise SOC estimates, which is crucial for optimizing battery management systems, particularly in electric vehicles.
Advanced thermal modeling of lithium-ion batteries: foundations for advanced capacity prediction Elkake, Abdelhadi; Laadissi, El Mehdi; Mossaddek, Meriem; Abdelhakim, Tabine; Hajajji, Abdelowahed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2699-2710

Abstract

Thermal modeling of lithium-ion batteries is crucial for optimizing their performance and reliability in applications such as electric vehicles and energy storage systems. This study introduces a novel thermal modeling framework to predict internal battery temperature as a function of current and ambient temperature. Three advanced methodologies, NN-LM, NN-BR, and GPM, were evaluated using drive cycle data across temperatures that vary from -20 °C to 25 °C. Among these, Gaussian process modeling (GPM) demonstrated the highest accuracy with an RMSE of 0.034%, while NN LM achieved an RMSE of 0.083%, offering a computationally efficient alternative suitable for real-time applications. The developed thermal model establishes a foundation for future research aimed at predicting battery capacity by incorporating the effects of internal temperature. Furthermore, accurate monitoring of internal temperature is critical for preventing thermal runaway by enabling early detection of unsafe thermal conditions. This work establishes a robust foundation for future research, aiming to develop real-time capacity prediction models, ultimately enhancing battery management systems under diverse operating conditions.
Co-Authors Abdelhakim, Tabine Bouzaid, Sohaib Elkake, Abdelhadi Ennawaoui, Chouaib Hajajji, Abdelowahed Hajjaji, Abdelowahed LAADISSI, EL MEHDI