An Energy Consumption Model for Electrical Vehicle Networks Via Extended Federated-Learning
Paper in proceeding, 2021

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.

Cooperative ITS

Electromobility

Energy-efficient Vehicles

Eco-driving

Author

Shiliang Zhang

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

IEEE Intelligent Vehicles Symposium, Proceedings

Vol. 2021-July 354-361

2021 IEEE Intelligent Vehicles Symposium (IV)
Nagoya, Japan,

Privacy-Protected Machine Learning for Transport Systems

AoA Transport Funds, 2020-06-15 -- 2021-06-14.

Privacy-preserving learning for vehicle networks: applications and tools

Swedish Research Council (VR), 2020-09-30 -- 2021-10-01.

Swedish Research Council (VR), 2020-09-30 -- 2022-07-01.

Subject Categories

Other Computer and Information Science

Other Engineering and Technologies

Areas of Advance

Information and Communication Technology

Transport

DOI

10.1109/iv48863.2021.9575223

More information

Latest update

11/23/2021