Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning
  Approach Equipped with Micro-Clustering and SMOTE Techniques
Hanif Tayarani Trisha V. Ramadoss Vaishnavi Karanam Gil Tal Christopher Nitta
Energy systems, climate change, and public health are among the primary
reasons for moving toward electrification in transportation. Transportation
electrification is being promoted worldwide to reduce emissions. As a result,
many automakers will soon start making only battery electric vehicles (BEVs).
BEV adoption rates are rising in California, mainly due to climate change and
air pollution concerns. While great for climate and pollution goals, improperly
managed BEV charging can lead to insufficient charging infrastructure and power
outages. This study develops a novel Micro Clustering Deep Neural Network
(MCDNN), an artificial neural network algorithm that is highly effective at
learning BEVs trip and charging data to forecast BEV charging events,
information that is essential for electricity load aggregators and utility
managers to provide charging stations and electricity capacity effectively. The
MCDNN is configured using a robust dataset of trips and charges that occurred
in California between 2015 and 2020 from 132 BEVs, spanning 5 BEV models for a
total of 1570167 vehicle miles traveled. The numerical findings revealed that
the proposed MCDNN is more effective than benchmark approaches in this field,
such as support vector machine, k nearest neighbors, decision tree, and other
neural network-based models in predicting the charging events.