Online Learning of Energy Consumption for Navigation of Electric Vehicles
Journal article, 2023

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 the 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.

Online learning

Energy efficient navigation

Multi-armed bandits

Thompson Sampling

Author

Niklas Åkerblom

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Volvo Cars

Yuxin Chen

University of Chicago

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Artificial Intelligence

0004-3702 (ISSN)

Vol. 317 103879

EENE: Energy Effective Navigation for EVs

FFI - Strategic Vehicle Research and Innovation (2018-01937), 2019-01-01 -- 2022-12-31.

Areas of Advance

Information and Communication Technology

Transport

Energy

Subject Categories

Energy Systems

Robotics

Computer Science

DOI

10.1016/j.artint.2023.103879

More information

Latest update

2/17/2023