Multimodule EV Charging: Communication Protocols Extension and Cloud-Based Optimization
IEEE Transactions on Industrial Informatics, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/tii.2026.3729732
- Dergi Adı: IEEE Transactions on Industrial Informatics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Communication protocols, electric vehicle (EV) charging, module borrowing, power electronics modules (PEMs)
- Gazi Üniversitesi Adresli: Evet
Özet
Module borrowing and multimodule electric vehicle (EV) charging are emerging techniques for enhancing the scalability of fast EV chargers; however, existing systems lack standardized communication and efficient coordination strategies for multimodule power sharing. This article presents a communication specification and an optimization framework for managing multimodule EV chargers. The proposed communication protocol extends the Open Charge Point Protocol and the power electronics protocol (PEP) specifications to ensure interoperability with existing standards while allowing advanced features for multimodule power sharing and power module borrowing. In addition, a cloud-based linear programming optimization framework is developed to determine the number of participating power electronic modules (PEMs) and the optimal power sharing ratios within the charging units. The framework dynamically selects contributing PEMs and distributes power usage across them to prevent early degradation of individual modules. To reduce computational burden in the cloud compared to the original linear programming solution, a neural network model is trained to act as a lower resource replacement for the optimizer. The proposed approach is experimentally validated using a lab-scale multimodule charging testbed with three PEMs across two ports, and the results demonstrate effective communication, coordinated power sharing, and reduced computational cost, with the neural network achieving approximately 4 ms inference time compared to 115 ms for the classical simplex solver at 100 PEMs.