Abstract：In response to the problems of insufficient consideration of charging pile resource constraints，low

practicality of time-domain discrete models，and inadequate description of peak shaving capabilities in the current

optimal scheduling of electric vehicles at charging stations，an optimal scheduling and peak shaving response

capability evaluation method for electric vehicles oriented to edge intelligence was proposed based on the

consideration of vehicle-pile matching. Firstly，an electric vehicle regulation and control architecture for charging

stations oriented to edge intelligence was proposed. Secondly，time-domain continuous variables were used to

represent the charging period，and a model of electric vehicle load and future available charging piles was

constructed，with the introduction of a vehicle-pile matching matrix and constraints. Taking the guarantee of user

charging demands as the primary goal and combining with the reduction of charging costs，an optimal scheduling

model was established. Then，considering the translation constraints of electric vehicle loads under different peak

shaving response capacities，a peak shaving response capability evaluation model was established. Finally，a typical

scenario of an actual charging station was analyzed as an example，and the proposed method was compared with the traditional method that does not consider matching charging piles and time-domain discrete models to verify its

effectiveness and practicality.

      Key words：edge intelligence；electric vehicle；charging pile；orderly charging；matching relation；demand

response capability