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Energy systems, climate change, and public health are among the primaryreasons for moving toward electrification in transportation. Transportationelectrification 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 andair pollution concerns. While great for climate and pollution goals, improperlymanaged BEV charging can lead to insufficient charging infrastructure and poweroutages. This study develops a novel Micro Clustering Deep Neural Network(MCDNN), an artificial neural network algorithm that is highly effective atlearning BEVs trip and charging data to forecast BEV charging events,information that is essential for electricity load aggregators and utilitymanagers to provide charging stations and electricity capacity effectively. TheMCDNN is configured using a robust dataset of trips and charges that occurredin California between 2015 and 2020 from 132 BEVs, spanning 5 BEV models for atotal of 1570167 vehicle miles traveled. The numerical findings revealed thatthe proposed MCDNN is more effective than benchmark approaches in this field,such as support vector machine, k nearest neighbors, decision tree, and otherneural network-based models in predicting the charging events.

Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques