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

arXiv: https://arxiv.org/abs/2511.03741 · HTML: https://mlsanity.com/paper/2511.03741/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Capturing Multivariate Dependencies of EV Charging Events: From Parametric Copulas to Neural Density Estimation](https://mlsanity.com/paper/2603.29554/) (2026)

## Abstract (arXiv)

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.