Szczegóły publikacji
Opis bibliograficzny
Cost optimization in microgrids: artificial intelligence-powered solutions for advanced battery management and longevity / Mohammed Amine Hoummadi, Badre Bossoufi, Mohammed Karim, Mohammed El Ghzaoui, Rachid El Alami, Paweł SKRUCH, Saleh Mobayen // International Journal of Electrical Power & Energy Systems ; ISSN 0142-0615 . — 2026 — vol. 174 art. no. 111505, s. 1-18. — Bibliogr. s. 17-18, Abstr. — Publikacja dostępna online od: 2026-01-06
Autorzy (7)
- Hoummadi Mohammed Amine
- Bossoufi Badre
- Karim Mohammed
- El Ghzaoui Mohammed
- El Alami Rachid
- AGHSkruch Paweł
- Mobayen Saleh
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165926 |
|---|---|
| Data dodania do BaDAP | 2026-03-09 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.ijepes.2025.111505 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | International Journal of Electrical Power & Energy Systems |
Abstract
This paper proposes an Artificial Intelligence (AI)- and Internet of Things (IoT)-enabled Battery Management System (BMS) for cost optimization and performance enhancement in microgrids. The framework integrates genetic algorithms to optimize charge–discharge cycles while leveraging real-time IoT sensor data for predictive monitoring and control. Simulation results demonstrate a 5% improvement in energy efficiency, up to 10% extension in battery life, and a 20% reduction in charge–discharge cycles. In addition, the proposed approach achieved cost savings of up to 10% by optimizing State of Charge (SoC), temperature, and operational frequency. These findings highlight the potential of AI- and IoT-driven BMS solutions to enhance the sustainability, reliability, and economic viability of microgrid energy storage systems.
