Capacity Degradation Mechanisms, Recovery Strategies, and Challenges of Flow Batteries
Corresponding Author: Tianshou Zhao
Nano-Micro Letters,
Vol. 19 (2027), Article Number: 44
Abstract
Redox flow batteries, by virtue of their advantages in power/capacity decoupling, high safety, and long cycle life, have become a strategic support for long-duration energy storage systems; however, capacity degradation remains a bottleneck for their commercialization. This review systematically identifies the multi-dimensional mechanisms of capacity degradation: including volume imbalance caused by transmembrane migration, inactivation and decomposition of active species, electrode degradation, and valence imbalance induced by side reactions. Targeting these challenges, the review deeply explores recovery strategies from physical repair to intelligent scheduling: covering the research and development of high-selectivity membranes, precise regulation of valence states, functionalized repair of electrodes, and model-driven predictive pathways integrating digital twins. Finally, current challenges such as characterization limitations and incomplete recovery rates are summarized, and future development directions for capacity recovery are envisioned. This review aims to provide a theoretical basis and technical guidance for extending the system cycle life of flow batteries and improving capacity recovery efficiency.
Highlights:
1 Systematically elucidates the physical (transmembrane migration-induced volume imbalance), chemical (inactivation of active species), and interfacial (electrode degradation, hydrogen/oxygen evolution) origins of capacity fade in redox flow batteries.
2 Summarizes recovery approaches from physical repair to intelligent scheduling, including high-selectivity membranes, precise valence regulation, electrode active-site regeneration, and model-driven collaborative scheduling.
3 Highlights bottlenecks (transient multi-physics coupling, kinetic limits at high current densities, stochastic predictive models) and future directions (operando multi-modal characterization, AI-accelerated smart materials discovery, digital-twin-based autonomous regulation).
Keywords
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