The increasing demand for lithium-ion batteries (LiBs) in electric vehicles, renewable energy storage and consumer electronics necessitates a transition towards high-performance, cost-effective, and sustainable manufacturing. Traditional manufacturing methods rely heavily on empirical trial-and-error approaches, leading to inefficiencies such as process variability, material waste and increased production costs. Recent advancements in digitalization, particularly artificial intelligence (AI) driven digital twins, offer transformative solutions to these challenges. This review presents a systematic framework for integrating AI and digital twin technologies into battery manufacturing, emphasizing their role in predictive maintenance, quality control, and process optimization. Unlike existing reviews, which primarily focus on theoretical modelling, this work examines real-world industrial applications, discusses challenges in large-scale AI adoption, and provides a practical roadmap for implementation. Key contributions include an analysis of digital models, shadows and twins in optimizing battery manufacturing processes and defect detection. Additionally, we highlight the contributions of Raw Material Suppliers, Battery Manufacturers, Technology and Innovation Partners, Policymakers and Regulators, along with actionable plans in establishing a fully digitalized battery production ecosystem. By bridging conventional manufacturing with intelligent digital frameworks, this review outlines a path toward scalable, high-quality, and sustainable battery production.

Data-driven battery state estimation has been widely studied in recent years to enable safe, reliable, and lifetime-aware battery management in e-mobility applications, where accurate estimation is essential under highly dynamic operating conditions. With several recent reviews focusing on model architectures and methodological developments, a critical assessment of practical feasibility, real-world deployability, and application-specific challenges in e-mobility is needed. This review therefore provides a challenge-oriented assessment of recent data-driven methods for battery state estimation, with particular emphasis on practical deployment in e-mobility systems. Beyond summarising existing approaches, it assesses how recent studies address key deployment barriers. These barriers include data dependency, limited ground-truth health labels, and fragmented datasets. Further challenges concern feature complexity and interpretability, robustness under domain shifts, embedded-BMS constraints, and cell-to-pack scalability.Recentresearch increasingly focuses on battery health analytics, reflecting the importance of ageing-aware battery management in e-mobility. Based on the gaps identified, future research directions are outlined to support the development of safe, reliable, interpretable, and deployment-ready battery state estimation for next-generation e-mobility systems.