Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles
This work addresses range anxiety for drivers of electric and hybrid vehicles, but it is incremental as it builds on existing data-driven methods.
The paper tackled the problem of range anxiety in hybrid electric vehicles by proposing a machine learning approach to model battery energy consumption, which reduced predictive uncertainty and improved accuracy compared to traditional methods.
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.