Data-driven probabilistic energy consumption estimation for battery electric vehicles with model uncertainty

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 behavior-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 behavior, 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 behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior 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.

KEYWORDS:

• Battery electric vehicles (BEVs)
• electric vehicles (EVs)
• energy consumption
• uncertainty
• neural networks
• probabilistic model
• range estimation
• driver Behaviour

Disclosure statement

No potential conflict of interest was reported by the author(s).

CRediT authorship contribution statement

Ayan Maity : Conceptualization, Methodology, Data Curation, Software, Validation, Visualization, Writing – Original Draft, Writing – Review and Editing. Sudeshna Sarkar : Conceptualization, Methodology, Supervision, Writing – Review and Editing.