Electric Vehicle Charging Load Modeling: A Survey, Trends, Challenges and Opportunities

TL;DR

This survey reviews recent EV charging load modeling approaches, categorizing methods, analyzing information fusion techniques, and discussing future challenges and opportunities for improving infrastructure planning.

Contribution

It provides a systematic analysis of recent modeling methods and information fusion techniques, highlighting gaps and guiding future research in EV charging load prediction.

Findings

Categorizes EV charging load models into statistical, simulated, and data-driven approaches.

Analyzes the three levels of information fusion in existing models.

Discusses key challenges and future opportunities in EV load modeling.

Abstract

The evolution of electric vehicles (EVs) is reshaping the automotive industry, advocating for more sustainable transportation practices. Accurately predicting EV charging behavior is essential for effective infrastructure planning and optimization. However, the charging load of EVs is significantly influenced by uncertainties and randomness, posing challenges for accurate estimation. Furthermore, existing literature reviews lack a systematic analysis of modeling approaches focused on information fusion. This paper comprehensively reviews EV charging load models from the past five years. We categorize state-of-the-art modeling methods into statistical, simulated, and data-driven approaches, examining the advantages and drawbacks of each. Additionally, we analyze the three bottom-up level operations of information fusion in existing models. We conclude by discussing the challenges and opportunities in the field, offering guidance for future research endeavors to advance our understanding and explore practical research directions.