Optimizing lithium-ion battery manufacturing with digitalization and AI-driven frameworks
Aaruththiran Manoharan, Jun Jie Chong, Zi Jie Choong, Stéphane Lambert, Raju Kumar Gupta, Dina Chandra, Akshay Jain, Amitej Rao, Anurag Sharma
Abstract
Abstract 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.