Machine learning-based battery life prediction and smart charging optimization for renewable energy storage systems

The fast development of renewable energy integration has amplified the demand for reliable, efficient, and intelligent energy storage solutions. Lithium-ion batteries take over current applications, however achieving accurate prediction of the state of charge (SoC), state of health (SoH), and optimizing charging strategies keep on challenging due to their nonlinear and aging-dependent behavior. This study suggests a hybrid machine learning framework that integrates long short-term memory (LSTM) networks for battery state forecast with a reinforcement learning (RL)-based smart charging controller . The novelty lies in the joint predictive–control design, where analytical visions from the LSTM model dynamically guide the RL agent for safe and enhanced charging. Relative analysis using benchmark datasets shows that the proposed model achieves an R² of 0.97 and RMSE of 0.067 for SoC prediction, overtaking traditional models. The RL-based charging policy achieves an 18% reduction in charging time, a 2.7% increase in charging efficiency, and a projected 15% improvement in cycle life compared to conventional CCCV charging. These findings point to the fact that, in addition to boosting battery reliability and lifetime, the combination of ML and RL can be used to facilitate the cost-effective storage of renewable energy, because it lowers the leveled cost of storage (LCOS) and optimizes the overall system sustainability. • ML improves battery SoC/SoH prediction accuracy (R² = 0.97) • RL-based charging reduces time by ~18% and extends life by ~15% • Ablation validates feature/data robustness • Framework lowers LCOS for renewable energy storage