Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time

TL;DR

This paper presents a hybrid CNN-BDT model that accurately estimates real-time energy consumption in electric vehicles, helping to reduce driver range anxiety without needing internal vehicle parameters.

Contribution

A novel hybrid CNN-BDT approach that estimates EV energy consumption in real-time without relying on internal vehicle parameters, improving accuracy over existing methods.

Findings

Achieves a mean absolute energy deviation of 0.14

Does not require internal vehicle parameters

Outperforms existing energy estimation techniques

Abstract

To overcome range anxiety problem of Electric Vehicles (EVs), an accurate real-time energy consumption estimation is necessary, which can be used to provide the EV's driver with information about the remaining range in real-time. A hybrid CNN-BDT approach has been developed, in which Convolutional Neural Network (CNN) is used to provide an energy consumption estimate considering the effect of temperature, wind speed, battery's SOC, auxiliary loads, road elevation, vehicle speed and acceleration. Further, Bagged Decision Tree (BDT) is used to fine tune the estimate. Unlike existing techniques, the proposed approach doesn't require internal vehicle parameters from manufacturer and can easily learn complex patterns even from noisy data. Comparison results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.