Data-Driven Probabilistic Energy Consumption Estimation for Battery Electric Vehicles with Model Uncertainty

TL;DR

This paper introduces a probabilistic neural network approach incorporating driver behavior and environmental factors to accurately estimate EV energy consumption with model uncertainty, aiding routing and charging planning.

Contribution

It presents a novel driver behavior-centric probabilistic neural network model with uncertainty estimation for EV energy consumption prediction, improving accuracy over existing models.

Findings

Achieved a mean absolute percentage error of 9.3%.

Outperformed existing EV energy consumption models.

Incorporated driver behavior factors like RPA, average acceleration, and deceleration.

Abstract

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behaviour-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behaviour and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration and average deceleration as driver behaviour factors in EV energy consumption estimation and this paper shows that the use of these driver behaviour features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy.