Abstract

Widespread electric vehicle (EV) adoption introduces new challenges for distribution grids due to large, localized load increases, stochastic charging behavior, and limited data availability. This paper proposes two data-driven methods to estimate residential EV charging profiles using real-world customer meter data from CenterPoint Energy serving the Houston area. The first approach applies a least-squares estimation to extract average charging rates by comparing aggregated EV and non-EV meter data, enabling a statistical method for starting and ending charge times. The second method isolates EV load from meter profiles and applies a kernel density estimation (KDE) to develop a probabilistic charging model. Both methods produce a distinct “u-shaped” daily charging profile, with most charging occurring overnight. The validated profiles offer a scalable tool for utilities to better anticipate EV-driven demand increases and support proactive grid planning.

Index Terms

Data-Driven, Electric Vehicles, Kernel Density Estimation, Load Profile Estimation, Probability Distributions.

Cite this paper:

Linhan Fang, Jesus Silva-Rodriguez, and Xingpeng Li, “Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation”, arXiv, Nov. 2025.

(Linhan Fang and Jesus Silva-Rodriguez contributed equally to this work.)