Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles

TL;DR

This paper presents a data-driven machine learning method to predict battery energy consumption in hybrid electric vehicles, aiming to reduce uncertainty and address range anxiety for better usability.

Contribution

It introduces a novel approach that leverages real-world data to improve prediction accuracy and reduce uncertainty in battery energy consumption models.

Findings

Improved predictive accuracy over traditional models

Reduced uncertainty in energy consumption predictions

Enhanced trust in vehicle performance

Abstract

The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.