A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques

Abstract

The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. In the literature, extensive studies have been conducted on model-based and probabilistic EV charging demand forecasting schemes. The studies provide a solid research foundation but result in complicated models with limited scalability. Meanwhile, emerging machine learning techniques bring promising prospects, yet exhibit suboptimal performance with insufficient data. Additionally, existing studies often overlook several critical areas such as overcoming data scarcity, security and privacy concerns, managing the inherent stochasticity of demand data, selecting forecasting methods for a specific feature, and developing standardized performance metrics. Considering the impact of the research topic, EV charging demand forecasting demands careful study. In this paper, we present a comprehensive survey of EV charging demand forecasting, focusing on both probabilistic and learning algorithms. First, we introduce the general procedure of EV charging demand forecasting, encompassing data sources, data pre-processing, and the key EV features. We then provide a taxonomy of existing EV charging demand forecasting techniques, followed by a critical analysis and comparative study of state-of-the-art research. Finally, we discuss open issues, which offer useful insights and future direction for various stakeholders.

Keywords

• Electric vehicle (EV)
• charging demand forecasting
• probabilistic model
• machine learning