Intelligent Modular Energy Hub: Advanced Optimization of Second-Life Lithium-Based Batteries for Sustainable Power Utilization
Yazarlar (1)
Dr. Öğr. Üyesi Abdulkadir GÖZÜOĞLU Tokat Gaziosmanpaşa Üniversitesi, Türkiye
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Özgün Makale
(Diğer hakemli uluslarası dergilerde yayınlanan tam makale)
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| Dergi Adı | Balkan Journal of Electrical and Computer Engineering | ||
| Dergi ISSN | 2147-284X | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | Türkçe | Basım Tarihi | 06-2025 |
| Cilt / Sayı / Sayfa | 13 / 2 / 219–229 | DOI | 10.17694/bajece.1641971 |
| Makale Linki | https://doi.org/10.17694/bajece.1641971 | ||
| UAK Araştırma Alanları |
Akıllı Elektrik Şebekeleri
Elektrik Enerjisi ve Güç Sistemleri
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| Özet |
| The Intelligent Modular Energy Hub (IMEH) introduces a cost-effective and scalable energy storage solution by repurposing second-life lithium-based batteries, including Li-ion, LiPo, and LiFePO₄ cells, sourced from discarded consumer electronics, power tools, and electric vehicles. This study develops an STM32- and ESP32-based battery testing system, integrating an electronic dummy load and a custom battery management system (BMS) to accurately assess the state-of-charge and state-of-health (SoH) of various battery chemistries. A 7S and variable parallel battery pack configuration ensures adaptability to diverse residential and off-grid applications. The proposed system features real-time IoT monitoring, extending battery lifespan while optimizing charging cycles through grid, solar, or wind energy sources. Experimental results demonstrate that the Samsung 25R battery exhibited the highest SoH (92%) and energy efficiency (95%), making it the most viable for second-life applications. The Turnigy Graphene LiPo battery, while displaying the highest efficiency (97%), showed a slightly lower capacity retention (89%), indicating potential limitations for long-term storage. Voltage drop analysis confirmed that lower internal resistance leads to better performance, with the Turnigy Graphene battery maintaining the lowest voltage drop (160mV) under discharge conditions. Additionally, the IMEH system achieved an average energy efficiency of 94.75%, outperforming commercial BMS solutions, which averaged 92% efficiency. IoT-based predictive maintenance enhanced battery longevity, ensuring better cycle count retention and charge … |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 2 |