AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments
The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/385 |
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| author | Md Tanjil Sarker Marran Al Qwaid Siow Jat Shern Gobbi Ramasamy |
| author_facet | Md Tanjil Sarker Marran Al Qwaid Siow Jat Shern Gobbi Ramasamy |
| author_sort | Md Tanjil Sarker |
| collection | DOAJ |
| description | The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. |
| format | Article |
| id | doaj-art-45f9cfd49cc1412c9daa5129c40b7175 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-45f9cfd49cc1412c9daa5129c40b71752025-08-20T03:32:28ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-07-0116738510.3390/wevj16070385AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential EnvironmentsMd Tanjil Sarker0Marran Al Qwaid1Siow Jat Shern2Gobbi Ramasamy3Centre for Electric Energy and High Voltage, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, MalaysiaDepartment of Computer Sciences, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaCentre for Electric Energy and High Voltage, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, MalaysiaCentre for Electric Energy and High Voltage, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, MalaysiaThe rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks.https://www.mdpi.com/2032-6653/16/7/385Artificial Intelligence (AI)smart electric vehicle chargingreinforcement learninggrid optimizationbattery energy storage system (BESS)solar photovoltaic (PV) |
| spellingShingle | Md Tanjil Sarker Marran Al Qwaid Siow Jat Shern Gobbi Ramasamy AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments World Electric Vehicle Journal Artificial Intelligence (AI) smart electric vehicle charging reinforcement learning grid optimization battery energy storage system (BESS) solar photovoltaic (PV) |
| title | AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments |
| title_full | AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments |
| title_fullStr | AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments |
| title_full_unstemmed | AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments |
| title_short | AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments |
| title_sort | ai driven optimization framework for smart ev charging systems integrated with solar pv and bess in high density residential environments |
| topic | Artificial Intelligence (AI) smart electric vehicle charging reinforcement learning grid optimization battery energy storage system (BESS) solar photovoltaic (PV) |
| url | https://www.mdpi.com/2032-6653/16/7/385 |
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