AI-Driven Digital Twin Frameworks for Predictive Maintenance and Process Optimization in Smart Battery Manufacturing for Electric Vehicles

Abstract

The increasing complexity and quality requirements of battery manufacturing demand intelligent, data-driven production frameworks capable of improving efficiency, reliability, and sustainability. Results demonstrate that the integration of predictive maintenance within the digital twin significantly reduces equipment downtime. Average monthly downtime decreased from 18.6 hours to 11.4 hours, accompanied by a reduction in variability, indicating improved operational stability. Accurate remaining useful life (RUL) prediction was achieved, with a strong correlation between predicted and actual degradation trends (R² ≈ 0.9+), supporting proactive maintenance planning and asset lifespan extension. Process quality improvements were further validated through a nearly 49% reduction in defect rates across production batches, highlighting the effectiveness of AI-driven parameter optimization. The framework also enhanced electrode coating thickness uniformity, reducing standard deviation from 7.8 µm to 3.0 µm while maintaining the target mean thickness, thereby improving electrochemical consistency and battery performance. Vibration-based anomaly detection enabled early fault identification, successfully detecting degradation progression prior to critical failure events. Reinforcement learning-based control demonstrated stable convergence, confirming its suitability for adaptive process optimization within a digital twin environment. Additionally, daily energy consumption was reduced by approximately 12–15% without compromising production throughput or quality. Comparative evaluation of multiple state-of-health (SOH) prediction models revealed that ensemble and attention-based architectures achieved superior accuracy and robustness. Overall, the results confirm that the proposed AI-driven digital twin framework provides a comprehensive solution for improving reliability, quality, energy efficiency, and decision-making in large-scale battery manufacturing systems.