A spatiotemporal distribution prediction model for electric vehicles charging load in transportation power coupled network.

ABSTRACT: With increasing demand of electric vehicles (EVs), problems such as insufficient EV charging piles and unreasonable layout of EV charging stations are also becoming prominent. New challenges are introduced to the planning of urban EV charging infrastructures. To effectively plan the charging facilities, accurately predicting EV charging loads is essential. The present study proposes a spatio-temporal distribution prediction model for EV charging loads in transportation-power coupled network (TPCN). First, path planning is performed separately using the Dijkstra algorithm and refined origin-destination (OD) probability matrix based on the travel characteristics of various vehicle types. The charging selection model is then formulated considering multiple compelling factors, such as transpor

SUBMITTER: Yang X 

PROVIDER: S-EPMC11787364 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature