Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles
This paper attempts to improve the performance of an off-grid electric vehicle charging station powered by photovoltaic (PV) panels and batteries. To ensure optimal performance of the PV panels, maximum power point tracking (MPPT) is implemented using an adaptive neuro-fuzzy inference system. A robu...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856147/ |
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| author | Bibi Tabassam Gul Iftikhar Ahmad Habibur Rehman Ammar Hasan |
| author_facet | Bibi Tabassam Gul Iftikhar Ahmad Habibur Rehman Ammar Hasan |
| author_sort | Bibi Tabassam Gul |
| collection | DOAJ |
| description | This paper attempts to improve the performance of an off-grid electric vehicle charging station powered by photovoltaic (PV) panels and batteries. To ensure optimal performance of the PV panels, maximum power point tracking (MPPT) is implemented using an adaptive neuro-fuzzy inference system. A robust control technique is designed for regulating the currents and voltages within the system. Conventional sliding mode controllers (SMCs) are prone to chattering, and while the inclusion of a super-twisting component mitigates this issue, it introduces windup problems. To address these challenges, we propose a conditioned-based super-twisting SMC (CST-SMC), which effectively resolves the windup issue. The controller gains are optimized using the grey wolf optimization algorithm, and global stability is demonstrated through Lyapunov stability analysis. The proposed system is simulated in MATLAB and experimentally validated using a Delfino F28379D-based hardware-in-the-loop setup. Numerical results show that the proposed CST-SMC has a faster rise time (0.0564 sec), less overshoot (0.00389), and a shorter settling time (0.4256 sec) compared to the conventional ST-SMC, which has a slower rise time (0.137 sec), greater overshoot (0.38095), and a longer settling time (3.8071 sec). Thus, the system’s dynamic response is improved. Furthermore, the proposed CST-SMC controller provides smoother regulation during the constant current and constant voltage stages, unlike the ST-SMC, which exhibits chattering. The improved performance makes the CST-SMC more reliable for EV charging. |
| format | Article |
| id | doaj-art-70de7a7d76e242eda7c2e2a9305144ae |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-70de7a7d76e242eda7c2e2a9305144ae2025-01-31T23:05:01ZengIEEEIEEE Access2169-35362025-01-0113203612037310.1109/ACCESS.2025.353557110856147Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric VehiclesBibi Tabassam Gul0Iftikhar Ahmad1https://orcid.org/0000-0002-2197-9890Habibur Rehman2https://orcid.org/0000-0002-8251-654XAmmar Hasan3https://orcid.org/0000-0003-2755-8410College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, PakistanCollege of Engineering, American University of Sharjah, Sharjah, United Arab EmiratesCollege of Engineering, American University of Sharjah, Sharjah, United Arab EmiratesThis paper attempts to improve the performance of an off-grid electric vehicle charging station powered by photovoltaic (PV) panels and batteries. To ensure optimal performance of the PV panels, maximum power point tracking (MPPT) is implemented using an adaptive neuro-fuzzy inference system. A robust control technique is designed for regulating the currents and voltages within the system. Conventional sliding mode controllers (SMCs) are prone to chattering, and while the inclusion of a super-twisting component mitigates this issue, it introduces windup problems. To address these challenges, we propose a conditioned-based super-twisting SMC (CST-SMC), which effectively resolves the windup issue. The controller gains are optimized using the grey wolf optimization algorithm, and global stability is demonstrated through Lyapunov stability analysis. The proposed system is simulated in MATLAB and experimentally validated using a Delfino F28379D-based hardware-in-the-loop setup. Numerical results show that the proposed CST-SMC has a faster rise time (0.0564 sec), less overshoot (0.00389), and a shorter settling time (0.4256 sec) compared to the conventional ST-SMC, which has a slower rise time (0.137 sec), greater overshoot (0.38095), and a longer settling time (3.8071 sec). Thus, the system’s dynamic response is improved. Furthermore, the proposed CST-SMC controller provides smoother regulation during the constant current and constant voltage stages, unlike the ST-SMC, which exhibits chattering. The improved performance makes the CST-SMC more reliable for EV charging.https://ieeexplore.ieee.org/document/10856147/MPPTANFISCST-SMCQBBCHILGWO |
| spellingShingle | Bibi Tabassam Gul Iftikhar Ahmad Habibur Rehman Ammar Hasan Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles IEEE Access MPPT ANFIS CST-SMC QBBC HIL GWO |
| title | Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles |
| title_full | Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles |
| title_fullStr | Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles |
| title_full_unstemmed | Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles |
| title_short | Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles |
| title_sort | optimized anfis based robust nonlinear control of a solar off grid charging station for electric vehicles |
| topic | MPPT ANFIS CST-SMC QBBC HIL GWO |
| url | https://ieeexplore.ieee.org/document/10856147/ |
| work_keys_str_mv | AT bibitabassamgul optimizedanfisbasedrobustnonlinearcontrolofasolaroffgridchargingstationforelectricvehicles AT iftikharahmad optimizedanfisbasedrobustnonlinearcontrolofasolaroffgridchargingstationforelectricvehicles AT habiburrehman optimizedanfisbasedrobustnonlinearcontrolofasolaroffgridchargingstationforelectricvehicles AT ammarhasan optimizedanfisbasedrobustnonlinearcontrolofasolaroffgridchargingstationforelectricvehicles |