Energy-Efficient Navigation of Electric Vehicles using Gaussian Processes

Abstract

Navigation software that prioritizes energy efficiency could, through simple means, help extend the effective range and adoption rate of electric vehicles (EV).

This thesis extends a previously studied online learning framework using Bayesian inference to find energy-efficient routes. In the extended framework, the characteristics of the road segments are combined with observed energy consumption data to provide probabilistic energy consumption estimates using Gaussian processes (GP). The GP uses a graph Matérn kernel extended from [1] and a feature kernel to model the correlation of energy consumption on separate road segments.

The framework is applied to a simple synthetic road network and real-world road networks in the traffic simulator SUMO. The results demonstrate that the GP learns more efficiently in the networks considered than in the Bayesian inference method. Furthermore, we investigate how the GP method is impacted by the number of inducing points, heteroskedastic noise modeling, an informative prior, and the choice of bandit algorithm.