A digital twin framework for intelligent electric vehicle charging optimization in smart manufacturing systems
Year: 2025
Type: article
Abstract: 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.
Source: Applied Energy
Authors: Chunting Liu, Ruyu Liu, Xiufeng Liu
Institution: Technical University of Denmark
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,430
OpenAlex ID: W7117114208