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Machine Learning-Based Charging Network Operation Service Platform Reservation Charging Service System

Authors:

Shu Su,

Hui Yan,

Ning DingAuthors Info & Claims

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

Pages 1 - 5

https://doi.org/10.1145/3297067.3297078

Published: 28 November 2018 Publication History

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Abstract

This paper proposes a machine learning-based electric vehicle (EV) reserved charging service system, which takes into consideration the impacts from both the power system and transportation system. The proposed framework of charging network operation service platform links the power system with transportation system through the charging navigation of massive EVs. The "reserved charging + consumption" integrated service model would be great significant for dealing with large-scale integration of electric vehicles. It applies the concept of charging time window to optimization of EV charging prediction for the reserved charging service system, and designs a dynamic dispatching model based on sliding time axis to make charging process of users get rid of constraints of queuing time and charging service fee period.

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Qiao FLin S(2024)Predicting Electrical Vehicle Charging Patterns at Public Charging Stations2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI62182.2024.10692786(329-334)Online publication date: 5-Jul-2024
https://doi.org/10.1109/BDAI62182.2024.10692786
Xu LXie S(2022)A Systemic Big Data Framework for the Charging Pile BusinessProceedings of the 7th International Conference on Information Systems Engineering10.1145/3573926.3573932(24-29)Online publication date: 4-Nov-2022
https://dl.acm.org/doi/10.1145/3573926.3573932

Index Terms

Machine Learning-Based Charging Network Operation Service Platform Reservation Charging Service System
1. Computer systems organization
  1. Real-time systems
    1. Real-time operating systems

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SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

November 2018

177 pages

ISBN:9781450366052

DOI:10.1145/3297067

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 November 2018

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the State Grid Corporation of China

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SPML '18

SPML '18: 2018 International Conference on Signal Processing and Machine Learning

November 28 - 30, 2018

Shanghai, China

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Qiao FLin S(2024)Predicting Electrical Vehicle Charging Patterns at Public Charging Stations2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI62182.2024.10692786(329-334)Online publication date: 5-Jul-2024
https://doi.org/10.1109/BDAI62182.2024.10692786
Xu LXie S(2022)A Systemic Big Data Framework for the Charging Pile BusinessProceedings of the 7th International Conference on Information Systems Engineering10.1145/3573926.3573932(24-29)Online publication date: 4-Nov-2022
https://dl.acm.org/doi/10.1145/3573926.3573932

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A holistic review on advanced bi-directional EV charging control algorithms

Optimal Control of PEVs with a Charging Aggregator Considering Regulation Service Provisioning

EV charging: separation of green and brown energy using IoT

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A holistic review on advanced bi-directional EV charging control algorithms

Optimal Control of PEVs with a Charging Aggregator Considering Regulation Service Provisioning

EV charging: separation of green and brown energy using IoT

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