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

Animesh Chattopadhyay, Subrat Kar

TL;DR

An Agent-based simulation framework that incorporates user optimism levels (pessimistic, standard, and optimistic) in the queue dynamics is introduced, which highlights the crucial role of human behaviour in shaping station efficiency for peak demand.

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.

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

TL;DR

An Agent-based simulation framework that incorporates user optimism levels (pessimistic, standard, and optimistic) in the queue dynamics is introduced, which highlights the crucial role of human behaviour in shaping station efficiency for peak demand.

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.
Paper Structure (14 sections, 13 equations, 15 figures, 2 tables, 2 algorithms)

This paper contains 14 sections, 13 equations, 15 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Proposed Charging Station Model: Theoretical Overview
  4. The Queueing Model
  5. Proposed Simulation Model
  6. Behavioral model of the EV User
  7. Proposed Informed Joining Implementation
  8. The 'Impatient' Queue
  9. Proposed EV Fast Charger Implementation
  10. Performance Evaluation
  11. Simulation settings
  12. Evaluation Metrics
  13. Results
  14. Conclusion

Figures (15)

  • Figure 1: The EV charging scenario, illustrating balking and reneging
  • Figure 2: Charging Profile (for two popular EV vehicles, BYD's E6 and Tata's Nexon, showing how the charging speed changes with SoC; the speed of charging decreases significantly after 80%. It takes roughly equal time to charge from 0% to 80% and to charge the remaining 20%TataNexo73:online
  • Figure 3: Most used charging duration lies between 100 and 200 minuteslee_acndata_2019
  • Figure 4: Charging traffic distribution over the Day recorded for a period of 6 months-- active hours are between 0700-2300 hours and the demand is higher between 0900-1000 hours and 1400-1900 hours lee_acndata_2019
  • Figure 5: Analytical modelling of the Wait Queue with reneging allowed for users
  • ...and 10 more figures