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Privacy-aware energy consumption modeling of connected battery electric vehicles using federated learning

Yan, Sen orcid logoORCID: 0000-0002-6860-3962, Fang, Hongyuan, Li, Ji orcid logoORCID: 0000-0001-7910-8011, Ward, Tomás E. orcid logoORCID: 0000-0002-6173-6607, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2023) Privacy-aware energy consumption modeling of connected battery electric vehicles using federated learning. IEEE Transactions on Transportation Electrification . ISSN 2332-7782

Abstract
Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Federated Learning (FL) is a promising solution mitigating the risk of exposing sensitive information to third parties by allowing local data to remain on devices and only sharing model updates with a central server. Our work investigates the potential of using FL methods, such as FedAvg, and FedPer, to improve BEV energy consumption prediction while maintaining user privacy. We conducted experiments using data from 10 BEVs under simulated real-world driving conditions. Our results demonstrate that the FedAvg-LSTM model achieved a reduction of up to 67.84\% in the MAE value of the prediction results. Furthermore, we explored various real-world scenarios and discussed how FL methods can be employed in those cases. Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Federated Learning; Electric Vehicles; Energy Consumption Modelling; Edge-Cloud Computing; Digital Twin; Privacy Awareness
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:IEEE
Official URL:https://doi.org/10.1109/TTE.2023.3343106
Copyright Information:© 2023 IEEE
Funders:Science Foundation Ireland (21/FFP-P/10266), Science Foundation Ireland (12/RC/2289_P2)
ID Code:29293
Deposited On:25 Jan 2024 12:50 by Mr Sen Yan . Last Modified 25 Jan 2024 12:54
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