Optimal EV Charging Decisions Considering Charging Rate Characteristics and Congestion Effects

TL;DR

This paper models EV charging decisions considering charging rate characteristics and congestion effects, providing analytical equilibrium strategies and insights into how different factors influence EV driver choices.

Contribution

It introduces a differential equation approach to analyze strategic EV charging decisions accounting for physical charging rates and congestion, which was not addressed in prior work.

Findings

Co-located EVs may choose different stations based on charging rate and battery level.

Charging rate characteristics significantly influence optimal strategies.

Fast-charging EVs can benefit slower-charging EV users in real-world scenarios.

Abstract

With the rapid growth in the demand for plug-in electric vehicles (EVs), the corresponding charging infrastructures are expanding. These charging stations are located at various places and with different congestion levels. EV drivers face an important decision in choosing which charging station to go to in order to reduce their overall time costs. However, existing literature either assumes a flat charging rate and hence overlooks the physical characteristics of an EV battery where charging rate is typically reduced as the battery charges, or ignores the effect of other drivers on an EV's decision making process. In this paper, we consider both the predetermined exogenous wait cost and the endogenous congestion induced by other drivers' strategic decisions, and propose a differential equation based approach to find the optimal strategies. We analytically characterize the equilibrium strategies and find that co-located EVs may make different decisions depending on the charging rate and/or remaining battery levels. Through numerical experiments, we investigate the impact of charging rate characteristics, modeling parameters and the consideration of endogenous congestion levels on the optimal charging decisions. Finally, we conduct numerical studies on real-world data and find that some EV users with slower charging rates may benefit from the participation of fast-charging EVs.