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https://doi.org/10.23919/acc45...
Article . 2020 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2019
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Inducing Human Behavior to Alleviate Overstay at PEV Charging Station

Authors: Bae, Sangjae; Zeng, Teng; Travacca, Bertrand; Moura, Scott;

Inducing Human Behavior to Alleviate Overstay at PEV Charging Station

Abstract

As the plug-in electric vehicle (PEV) market expands worldwide, PEV penetration has out-paced public PEV charging accessibility. In addition to charging infrastructure deployment, charging station operation is another key factor for improving charging service accessibility. In this paper, we propose a mathematical framework to optimally operate a PEV charging station, whose service capability is constrained by the number of available chargers. This mathematical framework specifically exploits human behavioral modeling to alleviate the "overstaying" issue that occurs when a vehicle is fully charged. Our behavioral model effectively captures human decision-making when humans are exposed to multiple charging product options, which differ in both price and quality-of-service. We reformulate the associated non-convex problem to a multi-convex problem via the Young-Fenchel transform. We then apply the Block Coordinate Descent algorithm to efficiently solve the optimization problem. Numerical experiments illustrate the performance of the proposed method. Simulation results show that a station operator who leverages optimally priced charging options could realize benefits in three ways: (i) net profits gains, (ii) overstay reduction, and (iii) increased quality-of-service.

Submitted to 2020 American Control Conference

Keywords

Signal Processing (eess.SP), FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
Green