Applications of Machine Learning in the Planning of Electric Vehicle Charging Stations and Charging Infrastructure: A Review-Reference-Cited by-同舟云学术

Applications of Machine Learning in the Planning of Electric Vehicle Charging Stations and Charging Infrastructure: A Review

Published:2022 Issue: Volume: Page:1-19
ISSN:
Container-title:Handbook of Smart Energy Systems
language:
Short-container-title:

Author:

Panda Bhagyashree,Rajabi Mohammad Sadra,Rajaee Alimohammad

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-72322-4_202-1

Reference51 articles.

1. S. Aghakhani, B. Mohammadi, M.S. Rajabi, A new hybrid multi-objective scheduling model for hierarchical hub and flexible flow shop problems. arXiv (2022).http://arXiv:2205.06465

2. Y.A. Alhazmi, H.A. Mostafa, M.M.A. Salama, Optimal allocation for electric vehicle charging stations using Trip Success Ratio. Int. J. Electr. Power Energy Syst. 91, 101–116 (2017)

3. P. Beigi, M. Haque, M.S. Rajabi, S. Hamdar, Bike share’s impact on COVID-19 transmission and bike share’s responses to COVID-19: a case study of Washington DC. arXiv (2022a).http://arXiv:2205.05011v1

4. P. Beigi, M. Khoueiry, M.S. Rajabi, S. Hamdar, Station reallocation and rebalancing strategy for bike-sharing systems: a case study of Washington DC. arXiv (2022b).http://arXiv:2204.07875

5. P. Beigi, M.S. Rajabi, S. Aghakhani, An overview of drone energy consumption factors and models. Arxiv (2022c).http://arXiv:2206.10775

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