GTS Press Journal of Sustainable Energy Environment and Green Technologies 1 1 2026 08 04 Reducing Battery Degradation through Intelligent Charge-Discharge Regulation for Extended Storage Lifespan 89 119 35 EN JSEEGT Author 4 Independent Researcher Journal Article 2026 06 25 The Battery management system performs an important role in ensuring safe operations, state estimation, energy efficiency and battery lifetime. For that matter, its efficiency relays in the comprehension of the process of battery degradation, its state of health development and charge or discharge processes at different operational conditions. Nevertheless, existing methodologies of Machine Learning (ML), physics models, Digital Twins (DT) and Deep Learning (DL) do not sufficiently incorporate battery physics and Artificial Intelligence (AI), the proper battery degradation description and the necessary adaptability to changing charge-discharge conditions. Therefore, this research proposed the Physics-Informed Twin Transformer Reinforcement Engine (P-ITTRE). This methodology includes Physics-Informed Neural Network (PINN), which provides battery degradation prediction through physics constraints, DT synchronization, which guarantees constant representation of battery states, Transformer Learning (TL), which used to make predictions regarding State of Health (SOH) and Remaining Useful Life (RUL), Reinforcement Learning (RL) for the adaptive regulation of charge/discharge processes and multi-objective optimization for balancing the process of battery degradation and lifetime. Experimentation proved 98.75% prediction accuracy and 40.2% degradation reduction, indicating improved predictive capability and effective degradation mitigation. Overall, P-ITTRE provides a predictive, adaptive, reliable and physics-aware solution for intelligent battery management and extended battery lifespan. #