Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning

TL;DR

This paper explores using federated learning to accurately predict electric vehicle energy consumption in real-time while preserving user privacy, demonstrating significant improvements over traditional methods.

Contribution

It introduces federated learning approaches for BEV energy modeling, showing their effectiveness in maintaining privacy and enhancing prediction accuracy.

Findings

FedAvg-LSTM reduced MAE by up to 67.84%.

FL methods improved prediction performance while safeguarding privacy.

Experiments conducted on data from 10 BEVs under simulated conditions.

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.