Our energy storage research leverages advanced AI-driven approaches to revolutionize the design, optimization, and lifecycle assessment of next-generation energy storage systems. By combining experimental testing, physics-based modeling, physics-informed machine learning, and generative design, we drive significant improvements in efficiency, performance, reliability, and sustainability. Our work spans the entire spectrum, from the chemistry within individual cells to pack-level thermal management, addressing applications in electric vehicles, grid-scale storage, and portable electronics.