Comparative Study of Fuzzy Logic and Neural Network Control for Battery Power Management in Smart Microgrids

Mabrouka Romdhane; Mohamed Naoui; Abdelmalek Gacem; Ali Mansouri

doi:10.2478/pead-2026-0023

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Comparative Study of Fuzzy Logic and Neural Network Control for Battery Power Management in Smart Microgrids

Power Electronics and Drives

Volume 11 (2026): Issue 1 (January 2026)

By: Mabrouka Romdhane , Mohamed Naoui , Abdelmalek Gacem and Ali Mansouri

Open Access

|Jul 2026

Abstract

This work presents a comparative study of two intelligent control strategies, fuzzy logic (FL) and artificial neural network (ANN), for a battery management system (BMS) within a grid-connected hybrid microgrid. The implementation and the evaluation of these intelligent controls were carried out using a real-world dataset under distribution grid instability constraints. The simulation results demonstrate that, while the fuzzy controller offers a faster dynamic response with high instantaneous power, it induces intensive battery loading characterised by frequent micro-cycling. On the other hand, the ANN-based control makes the power regulation smoother, thus minimising stress on the storage system and promoting energy efficiency. The economic analysis confirms the superiority of the neural approach, revealing a 17% reduction in energy costs compared to FL. These results highlight the crucial trade-off between response time and battery life preservation, positioning neural networks as a robust and cost-effective solution for the short-term management of smart microgrids.

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Articles in this issue

DOI: https://doi.org/10.2478/pead-2026-0023 | Journal eISSN: 2543-4292 | Journal ISSN: 2451-0262

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Language: English

Page range: 347 - 368

Submitted on: Mar 31, 2026

Accepted on: Jul 13, 2026

Published on: Jul 30, 2026

Published by: Wroclaw University of Science and Technology

In partnership with: Paradigm Publishing Services

Keywords:

battery management system,

renewable energy,

smart microgrid,

fuzzy logic,

artificial neural network

Related subjects:

Computer sciences,

Artificial intelligence,

Engineering,

Electrical engineering,

Electronics

© 2026 Mabrouka Romdhane, Mohamed Naoui, Abdelmalek Gacem, Ali Mansouri, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 11 (2026): Issue 1 (January 2026)