Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

Alam, Md Rakibul; Hou, Chuang; Aeschliman, Spencer; Zhou, Joann; Guo, Zhaomiao

doi:10.1080/19427867.2022.2070092

Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

Journal Article · Thu May 05 00:00:00 EDT 2022 · Transportation Letters

DOI:https://doi.org/10.1080/19427867.2022.2070092· OSTI ID:1991275

Alam, Md Rakibul ^[1]; Hou, Chuang ^[1]; Aeschliman, Spencer ^[2]; Zhou, Joann ^[2]; Guo, Zhaomiao ^[1]

University of Central Florida, Orlando, FL (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States). Energy System Division

Range anxiety remains one of the key concerns for ride-sourcing drivers to adopt battery electric vehicles (BEVs). To investigate the feasibility of using BEVs for ride-sourcing services, we propose an optimization-based methodology to estimate the daily driving trip patterns of ride-sourcing vehicles based on widely available non-identifiable trip data. Furthermore, we investigate the charging needs of electrified ride-sourcing vehicles using agent-based simulation. The methodologies are illustrated through a case study in the city of Chicago. Through sensitivity analysis on driver working hours and initial charging status, we quantify the range of daily average vehicle miles traveled (VMT) per car and identify the hot spots of current public charging demand and potential unsatisfied charging demand. This study can be used to determine the priorities of future charging infrastructure investment to further mitigate range anxiety and promote the adoption of electrified ride-sourcing services

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: Exelon Corporation; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1991275

Journal Information:: Transportation Letters, Journal Name: Transportation Letters Journal Issue: 6 Vol. 15; ISSN 1942-7867

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

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