Data-driven optimized layout of battery electric vehicle charging infrastructure
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In-vehicle sensors and communication data offer an opportunity to understand travel and charging behavior of EV owners, informing placement of charging stations or battery swapping stations.
“Also, in-vehicle sensors and communication data are increasingly becoming available and offer an opportunity to understand travel and charging behavior of EV owners, which can for example inform the placement of charging stations [779] or alternatives such as battery swapping stations.”
From Tackling climate change with machine learning (2023) · cites this paper as [779]
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- Authors
- Tao, Y., Huang, M., & Yang, L.
- Affiliations
- Wuhan University of Technology (CN)
- Corresponding author
- Huang, Miaohua
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 150
- Pages
- 735-744
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2790930344
- Cited by
- 76
- References
- 34
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 7 · 2023: 8 · 2022: 17 · 2021: 14 · 2020: 9 · 2019: 8 · 2018: 3
- FWCI
- 3.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Key Technology Support Program of China (2015BAG08B02)
- Fundamental Research Funds for the Central Universities (2017 IVA 037)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
- Keywords
- charging station location optimization, battery degradation, real-world driving data, genetic algorithm, slow charging, fast charging, budget cost
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