Data-Driven Probabilistic Energy Consumption Estimation for Battery
  Electric Vehicles with Model Uncertainty
Ayan Maity Sudeshna Sarkar
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.