Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption
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When a capsule network is combined with an improved routing approach for classification, it offers greater feature abstraction and representation capabilities without requiring a lot of data or parameters.
“When a capsule network (CapsNet) is combined with an improved routing approach (Xiao et al., 2020; Xiao et al., 2021) for classification, it offers greater feature abstraction and representation capabilities without requiring a lot of data or parameters.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as Xiao et al., 2021
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- Authors
- Xiao, Y., Zhang, Y., Kaku, I., Kang, R., & Pan, X.
- Affiliations
- Beihang University (CN)
- Tokyo City University (JP)
- Corresponding author
- Kang, Rui
- Pan, Xing
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 151
- Pages
- 111567
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3195019475
- Cited by
- 236
- References
- 78
- Citations by year
- 2026: 19 · 2025: 42 · 2024: 43 · 2023: 74 · 2022: 54 · 2021: 4
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China (71871003, 719710009)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- electric vehicle routing problem, electric vehicles, energy loss, piecewise linear approximation, emission cost, transport logistics, fleet planning
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