IDEAS: Information-Driven EV Admission in Charging Station Considering User Impatience to Improve QoS and Station Utilization

TL;DR

This paper presents an agent-based simulation framework for EV charging stations that incorporates user impatience and provides real-time wait time sharing to improve QoS and station throughput.

Contribution

It introduces a novel simulation framework considering human behavior and proposes real-time wait time sharing to reduce reneging and enhance station efficiency.

Findings

Real-time wait time sharing reduces reneging by up to 94%

Charging speed decreases beyond 80%, affecting user preferences

Two-mode charger design increases fast charging throughput by up to 5%

Abstract

Our work delves into user behaviour at Electric Vehicle(EV) charging stations during peak times, particularly focusing on how impatience drives balking (not joining queues) and reneging (leaving queues prematurely). We introduce an Agent-based simulation framework that incorporates user optimism levels (pessimistic, standard, and optimistic) in the queue dynamics. Unlike previous work, this framework highlights the crucial role of human behaviour in shaping station efficiency for peak demand. The simulation reveals a key issue: balking often occurs due to a lack of queue insights, creating user dilemmas. To address this, we propose real-time sharing of wait time metrics with arriving EV users at the station. This ensures better Quality of Service (QoS) with user-informed queue joining and demonstrates significant reductions in reneging (up to 94%) improving the charging operation. Further analysis shows that charging speed decreases significantly beyond 80%, but most users prioritize full charges due to range anxiety, leading to a longer queue. To address this, we propose a two-mode, two-port charger design with power-sharing options. This allows users to fast-charge to 80% and automatically switch to slow charging, enabling fast charging on the second port. Thus, increasing fast charger availability and throughput by up to 5%. As the mobility sector transitions towards intelligent traffic, our modelling framework, which integrates human decision-making within automated planning, provides valuable insights for optimizing charging station efficiency and improving the user experience. This approach is particularly relevant during the introduction phase of new stations, when historical data might be limited.