Coordinating Charging Request Allocation Between Self-Interested Navigation Service Platforms

Author:

Guillet Marianne12ORCID,Schiffer Maximilian23ORCID

Affiliation:

1. TomTom Location Technology Germany GmbH, 12435 Berlin, Germany;

2. School of Management, Technical University of Munich, 80333 Munich, Germany;

3. Munich Data Science Institute, Technical University of Munich, 80333 Munich, Germany

Abstract

Current electric vehicle market trends indicate an increasing adoption rate across several countries. To meet the expected growing charging demand, it is necessary to scale up the current charging infrastructure and to mitigate current reliability deficiencies, for example, due to broken connectors or misreported charging station availability status. However, even within a properly dimensioned charging infrastructure, a risk for local bottlenecks remains if several drivers cannot coordinate their charging station visit decisions. Here, navigation service platforms can optimally balance charging demand over available stations to reduce possible station visit conflicts and increase user satisfaction. Although such fleet-optimized charging station visit recommendations may alleviate local bottlenecks, they can also harm the system if self-interested navigation service platforms seek to maximize their own customers’ satisfaction. To study these dynamics, we model fleet-optimized charging station allocation as a resource allocation game in which navigation platforms constitute players and assign potentially free charging stations to drivers. We show that no pure Nash equilibrium guarantee exists for this game, which motivates us to study VCG mechanisms both in offline and online settings, to coordinate players’ strategies toward a better social outcome. Extensive numerical studies for the city of Berlin show that by coordinating players through VCG mechanisms, the social cost decreases on average by 42% in the online setting and by 52% in the offline setting. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms & Applications. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0269 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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