Quantum State Estimation for Real-Time Battery Health Monitoring in Photovoltaic Storage Systems

Abstract

The growing deployment of photovoltaic (PV) and energy storage systems (ESSs) in power grids has amplified concerns over component degradation, which undermines efficiency, increases costs, and shortens system lifespan. This paper proposes a quantum-enhanced optimization framework to mitigate degradation impacts in PV–storage systems through real-time adaptive energy dispatch. The framework combines quantum-assisted Monte Carlo simulation, quantum annealing, and reinforcement learning to model and optimize degradation pathways. A predictive maintenance module proactively adjusts charge–discharge cycles based on probabilistic forecasts of degradation states, improving resilience and operational efficiency. A hierarchical structure enables real-time degradation assessment, hourly dispatch optimization, and weekly long-term adjustments. The model is validated on a 5 MW PV array with a 2.5 MWh lithium-ion battery using real degradation profiles. Results demonstrate that the proposed framework reduces battery wear by 25% and extends PV module lifespan by approximately 2.5 years compared to classical methods. The hybrid quantum–classical implementation achieves scalable optimization under uncertainty, enabling faster convergence across high-dimensional solution spaces. This study introduces a novel paradigm in degradation-aware energy management, highlighting the potential of quantum computing to enhance both the sustainability and real-time control of renewable energy systems.