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Model Predictive Control for EV chargers coupling electro-thermal and degradation battery models.pdf (2.443Mb)
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Title
Model Predictive Control for EV Chargers Coupling Electro-Thermal and Degradation Battery Models
Author
Dorronsoro, Xabier
GARAYALDE, ERIK cc
IRAOLA, UNAI cc
Author (from another institution)
De Castro, Ricardo
Varela Barreras, Jorge
Publication Date
2023
Research Group
Almacenamiento de energía
Other institutions
https://ror.org/00d9ah105
Universitat Politècnica de València (UPV)
Version
Postprint
Document type
Conference ObjectConference Object
Language
English
Rights
© 2023 IEEE
Access
Embargoed access
URI
https://hdl.handle.net/20.500.11984/6398
Publisher’s version
https://doi.org/10.1109/VPPC60535.2023.10403342
Published at
IEEE Vehicle Power and Propulsion Conference (VPPC) 
Publisher
IEEE
Keywords
Electric vehicle
Charging stations
MPC
Batteries ... [+]
Electric vehicle
Charging stations
MPC
Batteries
cost optimization
ODS 7 Energía asequible y no contaminante
ODS 11 Ciudades y comunidades sostenibles
ODS 13 Acción por el clima [-]
Abstract
This paper presents an energy management algorithm for an Electric Vehicle (EV) charging station equipped with solar energy generation and local battery-based storage. For this purpose, a practical el ... [+]
This paper presents an energy management algorithm for an Electric Vehicle (EV) charging station equipped with solar energy generation and local battery-based storage. For this purpose, a practical electric, thermal, and aging model of a lithium-ion battery cell is developed. We then leverage this model to develop a Nonlinear Model Predictive Control (NL-MPC) to manage the energy flow between the battery, grid, solar generation and the EV charging loads. The NL-MPC aims to reduce the total operating electricity and battery depreciation costs, while taking into account temperature, state of charge, current and voltage constraints. Simulation results, based on a case study from a Spanish EV charging station, demonstrate the effectiveness of the proposed approach. [-]
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