Online Learning of Energy Consumption for Navigation of Electric Vehicles
Preprint, 2021

Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance in the single-agent and multi-agent settings, through an analysis of the algorithm under batched feedback. Finally, we demonstrate the performance of our methods via experiments on several real-world city road networks.

Författare

Niklas Åkerblom

Volvo Cars

Data Science och AI

Yuxin Chen

University of Chicago

Morteza Haghir Chehreghani

Data Science och AI

Styrkeområden

Informations- och kommunikationsteknik

Transport

Energi

Ämneskategorier

Energisystem

Robotteknik och automation

Datavetenskap (datalogi)

Relaterade dataset

arXiv:2111.02314 [dataset]

URI: https://arxiv.org/abs/2111.02314

Mer information

Skapat

2021-11-16