Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD
This study focuses on the control of an OFF-board electric vehicle (EV) charging station, providing a cost-efficient solution for managing high grid demand periods. By integrating a Kalman filter with Artificial Neural Networks (ANN) for Maximum Power Point Tracking (MPPT), the system optimizes ener...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Scientific African |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227625000341 |
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| author | Youness Hakam Hajar Ahessab Ahmed Gaga Mohamed Tabaa Benachir El Hadadi |
| author_facet | Youness Hakam Hajar Ahessab Ahmed Gaga Mohamed Tabaa Benachir El Hadadi |
| author_sort | Youness Hakam |
| collection | DOAJ |
| description | This study focuses on the control of an OFF-board electric vehicle (EV) charging station, providing a cost-efficient solution for managing high grid demand periods. By integrating a Kalman filter with Artificial Neural Networks (ANN) for Maximum Power Point Tracking (MPPT), the system optimizes energy capture from photovoltaic (PV) panels, even in severe weather conditions and partial shading. Unlike traditional MPPT methods, which face challenges with multiple peaks in the Power–Voltage (P–V) curve, the hybrid algorithm enhances tracking accuracy, reduces errors, and cuts tracking time by up to 99.93%. This ensures a reliable and sustainable power source for EV charging, reducing grid dependency during peak demand. On the inverter side, an innovative Model Predictive Control (MPC) strategy, using a K+2 step approach, is implemented to efficiently regulate the inverter. The system achieves a Total Harmonic Distortion (THD) of just 0.56%, boosting charging speed while minimizing harmonic distortion costs. Controlled by Texas Instruments’ TMS320F28379D digital signal processor, this system offers stable, low-cost EV charging by prioritizing solar energy use, even under harsh weather conditions, over reliance on grid power. |
| format | Article |
| id | doaj-art-9f7fb801893e43119b3cd6b0aa353034 |
| institution | Kabale University |
| issn | 2468-2276 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj-art-9f7fb801893e43119b3cd6b0aa3530342025-02-05T04:32:25ZengElsevierScientific African2468-22762025-03-0127e02563Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THDYouness Hakam0Hajar Ahessab1Ahmed Gaga2Mohamed Tabaa3Benachir El Hadadi4Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal, Morocco; Multidisciplinary Laboratory of Research and Innovation (LPRI), Moroccan School of Engineering Sciences (EMSI), Casablanca, Morocco; Corresponding author at: Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal, Morocco.Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal, MoroccoResearch Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal, MoroccoMultidisciplinary Laboratory of Research and Innovation (LPRI), Moroccan School of Engineering Sciences (EMSI), Casablanca, MoroccoResearch Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal, MoroccoThis study focuses on the control of an OFF-board electric vehicle (EV) charging station, providing a cost-efficient solution for managing high grid demand periods. By integrating a Kalman filter with Artificial Neural Networks (ANN) for Maximum Power Point Tracking (MPPT), the system optimizes energy capture from photovoltaic (PV) panels, even in severe weather conditions and partial shading. Unlike traditional MPPT methods, which face challenges with multiple peaks in the Power–Voltage (P–V) curve, the hybrid algorithm enhances tracking accuracy, reduces errors, and cuts tracking time by up to 99.93%. This ensures a reliable and sustainable power source for EV charging, reducing grid dependency during peak demand. On the inverter side, an innovative Model Predictive Control (MPC) strategy, using a K+2 step approach, is implemented to efficiently regulate the inverter. The system achieves a Total Harmonic Distortion (THD) of just 0.56%, boosting charging speed while minimizing harmonic distortion costs. Controlled by Texas Instruments’ TMS320F28379D digital signal processor, this system offers stable, low-cost EV charging by prioritizing solar energy use, even under harsh weather conditions, over reliance on grid power.http://www.sciencedirect.com/science/article/pii/S2468227625000341Hybrid Artificial Neural Networks-Kalman Filter (ANN-KF)Electric vehicleStation chargerPVMPPTModel Predictive Control (MPC) |
| spellingShingle | Youness Hakam Hajar Ahessab Ahmed Gaga Mohamed Tabaa Benachir El Hadadi Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD Scientific African Hybrid Artificial Neural Networks-Kalman Filter (ANN-KF) Electric vehicle Station charger PV MPPT Model Predictive Control (MPC) |
| title | Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD |
| title_full | Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD |
| title_fullStr | Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD |
| title_full_unstemmed | Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD |
| title_short | Design and simulation of a 5 KW solar-powered hybrid electric vehicle charging station with a ANN–Kalman filter MPPT and MPC-based inverter control for reduced THD |
| title_sort | design and simulation of a 5 kw solar powered hybrid electric vehicle charging station with a ann kalman filter mppt and mpc based inverter control for reduced thd |
| topic | Hybrid Artificial Neural Networks-Kalman Filter (ANN-KF) Electric vehicle Station charger PV MPPT Model Predictive Control (MPC) |
| url | http://www.sciencedirect.com/science/article/pii/S2468227625000341 |
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