Resource-Oriented Optimization of Electric Vehicle Systems: A Data-Driven Survey on Charging Infrastructure, Scheduling, and Fleet Management
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912571494236160 |
|---|---|
| author | Wang, Hai Guo, Baoshen Zhou, Xiaolei Wang, Shuai Hong, Zhiqing He, Tian |
| author_facet | Wang, Hai Guo, Baoshen Zhou, Xiaolei Wang, Shuai Hong, Zhiqing He, Tian |
| contents | Driven by growing concerns over air quality and energy security, electric vehicles (EVs) has experienced rapid development and are reshaping global transportation systems and lifestyle patterns. Compared to traditional gasoline-powered vehicles, EVs offer significant advantages in terms of lower energy consumption, reduced emissions, and decreased operating costs. However, there are still some core challenges to be addressed: (i) Charging station congestion and operational inefficiencies during peak hours, (ii) High charging cost under dynamic electricity pricing schemes, and (iii) Conflicts between charging needs and passenger service requirements.Hence, in this paper, we present a comprehensive review of data-driven models and approaches proposed in the literature to address the above challenges. These studies cover the entire lifecycle of EV systems, including charging station deployment, charging scheduling strategies, and large-scale fleet management. Moreover, we discuss the broader implications of EV integration across multiple domains, such as human mobility, smart grid infrastructure, and environmental sustainability, and identify key opportunities and directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04533 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resource-Oriented Optimization of Electric Vehicle Systems: A Data-Driven Survey on Charging Infrastructure, Scheduling, and Fleet Management Wang, Hai Guo, Baoshen Zhou, Xiaolei Wang, Shuai Hong, Zhiqing He, Tian Systems and Control Driven by growing concerns over air quality and energy security, electric vehicles (EVs) has experienced rapid development and are reshaping global transportation systems and lifestyle patterns. Compared to traditional gasoline-powered vehicles, EVs offer significant advantages in terms of lower energy consumption, reduced emissions, and decreased operating costs. However, there are still some core challenges to be addressed: (i) Charging station congestion and operational inefficiencies during peak hours, (ii) High charging cost under dynamic electricity pricing schemes, and (iii) Conflicts between charging needs and passenger service requirements.Hence, in this paper, we present a comprehensive review of data-driven models and approaches proposed in the literature to address the above challenges. These studies cover the entire lifecycle of EV systems, including charging station deployment, charging scheduling strategies, and large-scale fleet management. Moreover, we discuss the broader implications of EV integration across multiple domains, such as human mobility, smart grid infrastructure, and environmental sustainability, and identify key opportunities and directions for future research. |
| title | Resource-Oriented Optimization of Electric Vehicle Systems: A Data-Driven Survey on Charging Infrastructure, Scheduling, and Fleet Management |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.04533 |