Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning
This work addresses privacy concerns in energy consumption modeling for BEV users, though it is incremental as it applies existing FL methods to a specific domain.
The paper tackled the problem of predicting energy consumption for Battery Electric Vehicles while preserving user privacy, using Federated Learning methods like FedAvg-LSTM, which reduced MAE by up to 67.84% in experiments with data from 10 BEVs under simulated real-world conditions.
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.