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Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs
Tokyo Inst Technol, Sch Engn, Dept Syst & Control Engn, Tokyo 1528552, Japan.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0002-4876-0223
MIT, Lab Informat & Decis Syst, Cambridge, MA 02139 USA.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-1835-2963
2022 (English)In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 6358-6363Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a "green" routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can reduce inefficiency of equilibria.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 6358-6363
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-326420DOI: 10.1109/CDC51059.2022.9992570ISI: 000948128105054Scopus ID: 2-s2.0-85146972504OAI: oai:DiVA.org:kth-326420DiVA, id: diva2:1754282
Conference
IEEE 61st Conference on Decision and Control (CDC), DEC 06-09, 2022, Cancun, MEXICO
Note

QC 20230503

Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2025-03-19Bibliographically approved

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fulltext(744 kB)41 downloads
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Dán, GyörgySandberg, Henrik

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