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Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses

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Desheng ZhangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 1

Article No.: 13, Pages 1 - 26

https://doi.org/10.1145/3428080

Published: 25 November 2020 Publication History

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Abstract

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging, a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging, we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.

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Index Terms

Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
    2. Spatial-temporal systems
      1. Location based services

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 1

Regular Papers

February 2021

280 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3436534

Editor:
Yu Zheng
JD Digits, China

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Publication History

Published: 25 November 2020

Accepted: 01 October 2020

Revised: 01 August 2020

Received: 01 November 2019

Published in TIST Volume 12, Issue 1

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Shirvani SBaseri YGhorbani A(2024)Evaluation Framework for Electric Vehicle Security Risk AssessmentIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330766025:1(33-56)Online publication date: 1-Jan-2024
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Teimoori ZYassine ALu C(2024)Deep Transfer Learning for Detecting Electric Vehicles Highly Correlated Energy Consumption ParametersIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33587965:8(4087-4100)Online publication date: Aug-2024
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