A Privacy-Preserving Energy Management Strategy for Hybrid Storage Systems with Federated Learning Algorithm
| Authors | Geetha Anbazhagan1, U. Hemalatha2, V.Sudha3, JSanthakumar4, Usha S1 |
| Affiliations | 1Department of Electrical and Electronics Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, 603203 Kattankulathur, India 2Department of Artificial Intelligence and Data Science, Karpaga Vinayaga College of Engineering and Technology, Chennai, India 3Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, India 4Department of Mechanical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203 Chengalpattu, Tamil Nadu, India |
| Е-mail | ushas@srmist.edu.in |
| Issue | Volume 17, Year 2025, Number 6 |
| Dates | Received 10 August 2025; revised manuscript received December 2025; published online December 2025 |
| Citation | Geetha Anbazhagan, U. Hemalatha, V.Sudha, и др., J. Nano- Electron. Phys. 17 No 6, 06012 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(6).06012 |
| PACS Number(s) | 07.05.Kf, 88.85.Hj |
| Keywords | Energy management, Electric vehicle, Control strategy, Federated learning. |
| Annotation | Electric vehicle batteries need to be functional for as long as possible. This is achieved by means of Hybrid electric storage systems (HESS), which control, to a great extent, the power profiles of the charging as well as the discharging that directly impact the battery health. Hybrid Electric Storage System (HESS) integration extends battery life and optimizes energy management. In this paper, we introduce a novel energy management strategy (EMS) based on Federated Learning (FL) to address such challenges as unpredictable power demands can accelerate battery degradation. FL allows collaborative learning over multiple EVs, guarantees data privacy to facilitate accurate power demand prediction, and dynamic energy optimization. In the proposed FL based EMS, local data of individual EVs is fused and utilized to train prediction models which are aggregated into a global model. This approach is decentralized and utilizes the IoT ecosystem to improve the system-wide performance and scalability. The proposed approach is demonstrated in MATLAB simulations to reduce battery peak discharge power, minimize power variations, and increase energy efficiency. These results show the system’s capacity for increasing battery life, optimizing operational efficiency, and redefining energy management in real world EV deployment. |
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List of References English version of article |
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