Multi-Type Electric Vehicle Schedulability Modeling Method based on User Charging Behavior Characteristics

With the rapid proliferation of Electric Vehicles (EV s), effectively leveraging the regulation potential of EV charging loads has become a prominent issue. This paper proposes a multi-type EV schedulability modeling method based on the analysis of charging behavior characteristics of different user types. The model comprehensively considers the charging characteristics of various types of EV s, including private cars, ride-hailing vehicles, and taxis, utilizing a charging start-stop probability distribution model. Case studies demonstrate that this model can not only accurately reflect the charging demands of EV s but also provide valuable references for strategies involving EV fleet participation in grid regulation.

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Publication Details

Published
2024-09-12
DOI
https://doi.org/10.1109/ciced63421.2024.10754282
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi-Type Electric Vehicle Schedulability Modeling Method based on User Charging Behavior Characteristics

Qifang Chen, Haiqing Cai, Haohan Gu, Mingchao Xia et al.
Electric Vehicles and Infrastructure
article

Multi-Type Electric Vehicle Schedulability Modeling Method based on User Charging Behavior Characteristics

Qifang Chen, Haiqing Cai, Haohan Gu, Mingchao Xia, Yinjuan Xue
article en

Abstract

With the rapid proliferation of Electric Vehicles (EV s), effectively leveraging the regulation potential of EV charging loads has become a prominent issue. This paper proposes a multi-type EV schedulability modeling method based on the analysis of charging behavior characteristics of different user types. The model comprehensively considers the charging characteristics of various types of EV s, including private cars, ride-hailing vehicles, and taxis, utilizing a charging start-stop probability distribution model. Case studies demonstrate that this model can not only accurately reflect the charging demands of EV s but also provide valuable references for strategies involving EV fleet participation in grid regulation.

Beijing Jiaotong University (CN), China Southern Power Grid (China) (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 81%
Electric Vehicles and Infrastructure
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Multi-Type Electric Vehicle Schedulability Modeling Method based on User Charging Behavior Characteristics — Qifang Chen, Haiqing Cai, et al. · (2024) | TGRS Research Map | TGRS