AI Optimization for Hybrid Sources in Battery Management System
DOI:
https://doi.org/10.15662/IJEETR.2026.0802173Keywords:
Hybrid Renewable Energy System, Solar-Wind Integration, AI-Based Energy Management, , Battery Management System.Abstract
This paper presents a hybrid power generation system that solar power and wind such as renewable energy supports regular electricity settings. Integrate with mixture of power production in stations for renewable energy out turn intermittent possibility and differ the load requirements causes challenges combat. Battery cells in the mixing of in active will be enabled, battery management system (BMS) settings for charging in cycles improve and through safe limitations outside the cells running condition to prevention this reduce. A battery life times lasting besides change expenses in low conventional power system essential to ensure efficiency of power utilization is well for smart grid modern hybrid power system energy optimization. It is beneficial based on intelligent decision-making, the system adaptively and intelligently controls the power, optimizing battery performance. This results in reduced energy losses, improved power system reliability and efficient utilization of renewable energy sources. The proposed approach supports sustainable energy management and is suitable for modern smart grid applications
References
1. A. Riyaz, P. K. Sadhu, A. Iqbal and M. Tariq, “Power Management of Hybrid Grid System Using Artificial Neural Network,” Frontiers in Energy Research, 2021.
2. S. Rajamani and A. Ramasamy, “Battery Management in IoT Hybrid Grid System Using Deep Learning Algorithms,” Scientific Reports, 2025.
3. H. Guentri, T. Allaoui and M. Mekki, “Power Management and Control of a Photovoltaic System with Hybrid Battery-Supercapacitor Energy Storage,” Journal of Energy Storage, 2021.
4. K. S. Sankaran, C. Z. El-Bayeh and U. Eicker, “Design of Multi-Renewable Energy Storage and Management System Using MPPT Scheme,” Sustainability Journal, 2022.
5. B. Huang, W. Yu, M. Ma and X. Wei, “Artificial Intelligence Based Energy Management Strategies for Hybrid Electric Vehicles,” Energies, 2025.
6. S. Nazaralizadeh, P. Banerjee and A. K. Srivastava, “Battery Energy Storage Systems: A Review of Energy Management Systems, Energies, 2024.
7. J. V. Magsumbol, A. A. Bandala and A. B. Culaba, “Intelligent Battery Management in Hybrid Photovoltaic Systems Using Fuzzy Logic,” Technologies Journal, 2025.
8. N. E. Michaela, R. C. Bansal and A. A. Ismail, “Optimized Energy Management for Photovoltaic/Wind Hybrid Microgrid Using Energy Storage Solution,” International Journal of Modelling and Simulation, 2023.
9. A. Masih and H. K. Verma, “Renewable Hybrid Battery Energy Management System Using ANN Controller,” EasyChair Preprint, 2020.
10. A. Farakhor, D. Wu, Y. Wang and H. Fang, “A Modular Reconfigurable Battery Energy Storage System: Design and Control,” IEEE Energy Storage Systems, 2023.
11. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
12. Vimal, V. R., & Banerjee, J. S. (2025). Integrating PSO, GA, and ACO for Optimized ECG Feature Selection and Classification of Cardiac Disorders. SGS-Engineering & Sciences, 1(5).
