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Data-Driven Battery-Lifetime-Aware Scheduling for Electric Bus Fleets

Published: 14 September 2020 Publication History

Abstract

Electric vehicles (EVs) have experienced a sensational growth in the past few years, due to the potential of mitigating global warming and energy scarcity problems. However, the high manufacturing cost of battery packs and limited battery lifetime hinder EVs from further development. Especially, electric bus, as one of the most important means of public transportation, suffers from long daily operation time and peak-hour passenger overload, which aggravate its battery degradation. To address this issue, we propose a novel data-driven battery-lifetime-aware electric bus scheduling system. Leveraging practical bus GPS and transaction datasets, we conduct a detailed analysis of passenger behaviors and design a reliable prediction model for passenger arrival rate at each station. By taking passenger waiting queue at each bus station analogous to data buffer in network systems, we apply Lyapunov optimization and obtain an electric bus scheduling strategy with reliable performance guarantee on both battery degradation rate and passengers' service quality. To verify the effectiveness of the system, we evaluate our design on a 12-month electric bus operation datasets from the city of Shenzhen. The experimental results show that, compared with two baseline methods, our system reduces the battery degradation rate by 14.3% and 21.7% under the same passenger arrival rate, while preserving good passenger service quality.

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Cited By

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  • (2024)Real-world Electric Bus Operation: Trend in Technology, Performance, Degradation, and Lifespan of BatteriesWorld Resources Institute10.46830/wriwp.22.00097Online publication date: Jan-2024
  • (2023)Extending Delivery Range and Decelerating Battery Aging of Logistics UAVs Using Public BusesIEEE Transactions on Mobile Computing10.1109/TMC.2022.316704022:9(5280-5295)Online publication date: 1-Sep-2023
  • (2023)Optimizing Cross-Line Dispatching for Minimum Electric Bus FleetIEEE Transactions on Mobile Computing10.1109/TMC.2021.311942122:4(2307-2322)Online publication date: 1-Apr-2023
  • Show More Cited By

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    Published In

    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 3, Issue 4
    December 2019
    873 pages
    EISSN:2474-9567
    DOI:10.1145/3375704
    Issue’s Table of Contents
    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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    Publication History

    Published: 14 September 2020
    Published in IMWUT Volume 3, Issue 4

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    Author Tags

    1. battery degradation
    2. bus scheduling optimization
    3. data-driven scheduling system
    4. electric bus fleet
    5. passenger flow prediction

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    View all
    • (2024)Real-world Electric Bus Operation: Trend in Technology, Performance, Degradation, and Lifespan of BatteriesWorld Resources Institute10.46830/wriwp.22.00097Online publication date: Jan-2024
    • (2023)Extending Delivery Range and Decelerating Battery Aging of Logistics UAVs Using Public BusesIEEE Transactions on Mobile Computing10.1109/TMC.2022.316704022:9(5280-5295)Online publication date: 1-Sep-2023
    • (2023)Optimizing Cross-Line Dispatching for Minimum Electric Bus FleetIEEE Transactions on Mobile Computing10.1109/TMC.2021.311942122:4(2307-2322)Online publication date: 1-Apr-2023
    • (2022)Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316906823:10(19484-19496)Online publication date: Oct-2022
    • (2021)Sustainability Assessment of Public Transport, Part II—Applying a Multi-Criteria Assessment Method to Compare Different Bus TechnologiesSustainability10.3390/su1303127313:3(1273)Online publication date: 26-Jan-2021
    • (2020)A generative simulation platform for multi-agent systems with incentivesAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414590(580-587)Online publication date: 10-Sep-2020

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