A digital twin framework for intelligent electric vehicle charging optimization in smart manufacturing systems

The electrification of industrial vehicle fleets introduces complex coordination challenges in dynamic manufacturing environments, where vehicle availability directly influences operational continuity. This paper proposes a novel Digital Twin (DT) framework that integrates discrete-event simulation with a multi-objective optimization engine for intelligent electric vehicle (EV) charging. The system employs a hierarchical rolling-horizon strategy that accounts for battery states, production demands, and dynamic electricity pricing. Simulation studies across four representative manufacturing scenarios, evaluating five charging strategies including uncontrolled, first-come-first-served (FCFS), and our intelligent optimization, demonstrate the effectiveness of the proposed approach. Results reveal that the intelligent strategy delivers substantial energy cost reductions (up to 54.4 %), improved carbon efficiency, and increased infrastructure utilization. Compared to FCFS, which incurs 36.4–37.2 % higher energy and emission burdens, the intelligent framework consistently supports more sustainable and efficient charging. Scenario-specific variations in operational throughput offer opportunities for adaptive algorithmic refinement. These findings provide a scalable, modular, and data-driven solution for integrating EV charging infrastructure as a co-optimized component of smart manufacturing systems. • Digital Twin framework for intelligent EV charging in smart manufacturing. • Achieves up to 54.4 % energy cost reduction and 45.3 % lower carbon emissions. • Multi-objective optimization balances energy costs, vehicle availability, and production. • Reveals critical trade-off between cost optimization and operational throughput.