Machine learning-enhanced digital twins for predictive analytics in battery pack assembly

The electric vehicle (EV) market is rapidly growing, with battery modules playing a central role in this transformation. However, optimizing production throughput in battery module assembly is challenging due to the complexity of multi-stage processes and bottlenecks that limit overall efficiency. Traditional solutions, such as direct shop floor adjustments, simulation models, and digital twins (DT), can be costly and less scalable. This study proposes a digital twin surrogate (DTS) model, integrating machine learning techniques—Linear Regression, Support Vector Regression, K-Nearest Neighbors, Random Forest Regression, Deep Neural Networks , XGBoost, and Long Short-Term Memory networks—to estimate throughput and predict future machine states. The impact of dataset size and aggregation methods on model performance is also examined, providing shop managers with insights into how production line variations affect throughput. • ML and DL-based digital twin surrogates for RESS production line. • Digital twin surrogate used to predict and optimize throughput in RESS. • Real-time performance estimation models to improve throughput and reduce bottlenecks in EV battery manufacturing. • Examines trade-offs between computational speed and model accuracy. • Investigates impact of dataset size and aggregation on model scalability.