Projects per year
Abstract
Model predictive control (MPC), due to various advantages, is considered a potential control algorithm in high-level control of charging stations. Using proper cost function and optimization techniques, addressing the issues of charging stations in an optimal path can be easily achieved with MPC. Nowadays, considering the set target of the electric transportation fleet, trucks are not exempt. Mega charging stations (MCS) are suggested as a solution to decrease charging time. In this paper, an MPC-based charging management strategy is developed to address the main issues of MCS (efficiency and charging time). The proposed strategy is simulated in MATLAB. The algorithm is also compared with two other approaches to get validated on different aspects. The results are promising, with a dominant decrease in average energy loss in 2000 random charging events. The EV charging model and a low fidelity model are also developed in Simulink/MATLAB to evaluate the mathematical model's accuracy.
Original language | English |
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Title of host publication | 2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) |
Pages | 351-356 |
Number of pages | 6 |
ISBN (Electronic) | 9798350387599 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) - Ischia, Napoli, Italy Duration: 19 Jun 2024 → 21 Jun 2024 https://www.speedam.org/ |
Publication series
Name | 2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2024 |
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Conference
Conference | 2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) |
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Abbreviated title | SPEEDAM |
Country/Territory | Italy |
City | Napoli |
Period | 19/06/24 → 21/06/24 |
Internet address |
Bibliographical note
Funding Information:This project has received funding from the European Union s Horizon Europe Research and Innovation programme under Grant Agreement No.101056740. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can held responsible for them. The authors also acknowledge Flanders Make for the support to our research group.
Publisher Copyright:
© 2024 IEEE.
Keywords
- mega charging station
- charging strategy
- heavy-duty~electric truck
- Model predictive control
Projects
- 1 Active
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EUAR80: NextETRUCK : Efficient and affordable Zero Emission logistics through NEXT generation Electric TRUCKs.
Hegazy, O., Varga, E. F., Geury, T., El Baghdadi, M., Tran, D., Ghadirzadeh, A. & Hruz, A.
1/07/22 → 31/12/25
Project: Applied