Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control
This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024014476 |
| _version_ | 1826927504584081408 |
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| author | Aziz Watil Hamid Chojaa |
| author_facet | Aziz Watil Hamid Chojaa |
| author_sort | Aziz Watil |
| collection | DOAJ |
| description | This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller. |
| first_indexed | 2025-02-17T15:34:02Z |
| format | Article |
| id | doaj.art-dce608821c0c478f8e0c15ab3c8e84e1 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2025-02-17T15:34:02Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-dce608821c0c478f8e0c15ab3c8e84e12024-12-19T10:58:30ZengElsevierResults in Engineering2590-12302024-12-0124103192Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive controlAziz Watil0Hamid Chojaa1School of Aerospace and Automotive Engineering, LERMA Laboratory, International University of Rabat, Rabat 11100, Morocco; Corresponding author.Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoThis paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.http://www.sciencedirect.com/science/article/pii/S2590123024014476Electric vehicle charging stationPV powerLi-ion batteryMPPTPILMPC |
| spellingShingle | Aziz Watil Hamid Chojaa Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control Results in Engineering Electric vehicle charging station PV power Li-ion battery MPPT PIL MPC |
| title | Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control |
| title_full | Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control |
| title_fullStr | Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control |
| title_full_unstemmed | Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control |
| title_short | Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control |
| title_sort | enhancing grid connected pv ev charging station performance through a real time dynamic power management using model predictive control |
| topic | Electric vehicle charging station PV power Li-ion battery MPPT PIL MPC |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024014476 |
| work_keys_str_mv | AT azizwatil enhancinggridconnectedpvevchargingstationperformancethrougharealtimedynamicpowermanagementusingmodelpredictivecontrol AT hamidchojaa enhancinggridconnectedpvevchargingstationperformancethrougharealtimedynamicpowermanagementusingmodelpredictivecontrol |