The research is intended to create an intelligent battery management system for accurate estimation of battery states, prediction and optimization of degradation. However, the existing battery management systems based on the application of machine learning algorithms, physical models, digital twins, deep learning algorithms usually face the challenges such as limited integration of battery physics and AI prediction, adaptability during dynamic operation, imprecise estimation of degradationand lack of intelligent charge/discharge control. In this context, the new Physics-Informed Digital Twin Transformer Reinforcement Adaptive Battery Optimizer (PDT-TRABO) will be able to deal with the above challenges due to the possibility of using physics-informed neural networks for degradation-aware modelling, digital twins for real-time synchronization of the battery state, transformer temporal learning for SOH and RUL prediction, reinforcement learning for adaptive charge/discharge control and multi-objective optimization for energy efficiency. Thus, the novel algorithm makes it possible to reach 98.75% accuracy of prediction, 1.2% SOH estimation error, 1.6% RUL prediction error, 97.8% energy efficiency, 40.2% degradation reduction and 44.5% cycle life extension compared to other battery management approaches. Moreover, p-test and t-test analyses show the statistical superiority of the algorithm. Overall, the PDT-TRABO battery management method appears to be highly predictive and adaptive.