Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms
This paper presents a smart electric vehicle (EV) charging management system that integrates Reinforcement Learning intelligence on a Field-Programmable Gate Array (FPGA) platform. The system is based on the Q-learning algorithm, where the RL agent perceives environmental conditions, captured throug...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2585 |
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| author | Udhaya Mugil Damodarin Gian Carlo Cardarilli Luca Di Nunzio Marco Re Sergio Spanò |
| author_facet | Udhaya Mugil Damodarin Gian Carlo Cardarilli Luca Di Nunzio Marco Re Sergio Spanò |
| author_sort | Udhaya Mugil Damodarin |
| collection | DOAJ |
| description | This paper presents a smart electric vehicle (EV) charging management system that integrates Reinforcement Learning intelligence on a Field-Programmable Gate Array (FPGA) platform. The system is based on the Q-learning algorithm, where the RL agent perceives environmental conditions, captured through hardware sensors such as current, voltage, and priority indicators, and makes optimal charging decisions to address grid stress and prioritize charging needs. The FPGA implementation leverages hardware design strategies to ensure efficient operation and real-time response within a limited amount of required energy, allowing for its implementation in embedded applications and possibly enabling the use of an energy harvesting power source, like a small solar panel. The proposed design effectively manages multiple EV chargers by dynamically allocating current and prioritizing charging tasks to maintain service quality. Through intelligent decision making, informed by continuous sensor feedback, the system adapts to fluctuating grid conditions and optimizes energy distribution. Key findings highlight the system’s ability to maintain stable operation under varying demand conditions, improving power efficiency, safety, and service reliability. Moreover, the design is scalable, enabling seamless expansion for larger installations by following consistent architectural guidelines. This FPGA-based solution combines RL intelligence, sensor-based environmental perception, and robust hardware design, offering a practical framework for an efficient EV charging infrastructure in modern smart grid environments. |
| format | Article |
| id | doaj-art-1864cbf7a7f648bebe561453905b68f0 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1864cbf7a7f648bebe561453905b68f02025-08-20T02:25:07ZengMDPI AGSensors1424-82202025-04-01258258510.3390/s25082585Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA PlatformsUdhaya Mugil Damodarin0Gian Carlo Cardarilli1Luca Di Nunzio2Marco Re3Sergio Spanò4Department of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, ItalyDepartment of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, ItalyThis paper presents a smart electric vehicle (EV) charging management system that integrates Reinforcement Learning intelligence on a Field-Programmable Gate Array (FPGA) platform. The system is based on the Q-learning algorithm, where the RL agent perceives environmental conditions, captured through hardware sensors such as current, voltage, and priority indicators, and makes optimal charging decisions to address grid stress and prioritize charging needs. The FPGA implementation leverages hardware design strategies to ensure efficient operation and real-time response within a limited amount of required energy, allowing for its implementation in embedded applications and possibly enabling the use of an energy harvesting power source, like a small solar panel. The proposed design effectively manages multiple EV chargers by dynamically allocating current and prioritizing charging tasks to maintain service quality. Through intelligent decision making, informed by continuous sensor feedback, the system adapts to fluctuating grid conditions and optimizes energy distribution. Key findings highlight the system’s ability to maintain stable operation under varying demand conditions, improving power efficiency, safety, and service reliability. Moreover, the design is scalable, enabling seamless expansion for larger installations by following consistent architectural guidelines. This FPGA-based solution combines RL intelligence, sensor-based environmental perception, and robust hardware design, offering a practical framework for an efficient EV charging infrastructure in modern smart grid environments.https://www.mdpi.com/1424-8220/25/8/2585sensors data processingreinforcement learningQ-learningFPGAhardware accelerationgreen mobility |
| spellingShingle | Udhaya Mugil Damodarin Gian Carlo Cardarilli Luca Di Nunzio Marco Re Sergio Spanò Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms Sensors sensors data processing reinforcement learning Q-learning FPGA hardware acceleration green mobility |
| title | Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms |
| title_full | Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms |
| title_fullStr | Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms |
| title_full_unstemmed | Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms |
| title_short | Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms |
| title_sort | smart electric vehicle charging management using reinforcement learning on fpga platforms |
| topic | sensors data processing reinforcement learning Q-learning FPGA hardware acceleration green mobility |
| url | https://www.mdpi.com/1424-8220/25/8/2585 |
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