Artificial Intelligence-driven control of lithium-ion battery systems for improved grid integration and power supply optimization

The rapid proliferation of renewable energy sources has underscored the critical need for robust energy storage solutions to ensure grid stability and efficient power delivery. Lithium-ion battery (LIB) systems stand at the forefront of this transition, yet their performance in grid-scale applications is often hampered by challenges such as degradation, thermal instabilities, and suboptimal integration. This review explores the transformative potential of artificial intelligence (AI)-driven control strategies in overcoming these barriers, synthesizing recent advancements in machine learning, deep learning, and reinforcement learning for battery management systems (BMS). By examining AI applications in state estimation, thermal management, grid stability, and power supply optimization, the paper highlights how these technologies enable precise energy dispatch, enhance demand response, and facilitate seamless coordination with smart grids. Key insights reveal significant improvements in LIB efficiency, lifespan extension, and economic viability, while also yielding environmental benefits through reduced carbon emissions and minimized renewable curtailment. Ultimately, this work illuminates the path toward resilient, sustainable power infrastructures, identifying persistent challenges like data dependency and computational demands, alongside promising avenues for innovation. As AI continues to evolve, its integration with LIB systems promises to redefine grid dynamics, offering a compelling blueprint for future energy systems that balance reliability, cost, and ecological imperatives.