Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques
This addresses the need for electricity load aggregators and utility managers to manage BEV charging effectively, though it is incremental as it builds on existing neural network methods with micro-clustering and SMOTE techniques.
The study tackled the problem of forecasting battery electric vehicle (BEV) charging events to prevent infrastructure shortages and power outages, developing a Micro Clustering Deep Neural Network (MCDNN) that outperformed benchmark models like SVM and decision trees on a dataset of 132 BEVs in California.
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.