An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning

TL;DR

This paper introduces an extended federated-learning model for electric vehicle networks that accurately estimates battery consumption and optimizes energy-efficient routing, addressing range anxiety without compromising data privacy.

Contribution

It extends federated learning with anomaly detection and sharing policies to improve robustness and accuracy in heterogeneous EV data environments.

Findings

Higher accuracy in battery consumption estimation.

Maintains efficiency without increasing complexity.

No raw data transmission needed.

Abstract

Electrical vehicle (EV) raises to promote an eco-sustainable society. Nevertheless, the "range anxiety" of EV hinders its wider acceptance among customers. This paper proposes a novel solution to range anxiety based on a federated-learning model, which is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks. Specifically, the new approach extends the federated-learning structure with two components: anomaly detection and sharing policy. The first component identifies preventing factors in model learning, while the second component offers guidelines for information sharing amongst vehicle networks when the sharing is necessary to preserve learning efficiency. The two components collaborate to enhance learning robustness against data heterogeneities in networks. Numerical experiments are conducted, and the results show that compared with considered solutions, the proposed approach could provide higher accuracy of battery-consumption estimation for vehicles under heterogeneous data distributions, without increasing the time complexity or transmitting raw data among vehicle networks.