IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i8p2060-d162604.html
   My bibliography  Save this article

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Author

Listed:
  • Yajing Gao

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Shixiao Guo

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Jiafeng Ren

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Zheng Zhao

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Ali Ehsan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yanan Zheng

    (Energy Research Institute, Academy of Macroeconomic Research, NDRC, Beijing 100038, China)

Abstract

With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

Suggested Citation

  • Yajing Gao & Shixiao Guo & Jiafeng Ren & Zheng Zhao & Ali Ehsan & Yanan Zheng, 2018. "An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors," Energies, MDPI, vol. 11(8), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2060-:d:162604
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/8/2060/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/8/2060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fanrong Kong & Jianhui Jiang & Zhigang Ding & Junjie Hu & Weian Guo & Lei Wang, 2017. "A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm," Energies, MDPI, vol. 10(9), pages 1-18, September.
    2. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    3. Shuyu Dai & Dongxiao Niu & Yan Li, 2018. "Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm," Energies, MDPI, vol. 11(1), pages 1-25, January.
    4. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    5. Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    6. Lajunen, Antti & Lipman, Timothy, 2016. "Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses," Energy, Elsevier, vol. 106(C), pages 329-342.
    7. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    8. Sidra Mumtaz & Saima Ali & Saghir Ahmad & Laiq Khan & Syed Zulqadar Hassan & Tariq Kamal, 2017. "Energy Management and Control of Plug-In Hybrid Electric Vehicle Charging Stations in a Grid-Connected Hybrid Power System," Energies, MDPI, vol. 10(11), pages 1-21, November.
    9. S. Muhammad Bagher Sadati & Jamal Moshtagh & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner," Energies, MDPI, vol. 10(11), pages 1-16, October.
    10. Ana-Irina Stroe & Jinhao Meng & Daniel-Ioan Stroe & Maciej Świerczyński & Remus Teodorescu & Søren Knudsen Kær, 2018. "Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries," Energies, MDPI, vol. 11(4), pages 1-19, March.
    11. Ahmed Abdalrahman & Weihua Zhuang, 2017. "A Survey on PEV Charging Infrastructure: Impact Assessment and Planning," Energies, MDPI, vol. 10(10), pages 1-25, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boud Verbrugge & Abdul Mannan Rauf & Haaris Rasool & Mohamed Abdel-Monem & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2022. "Real-Time Charging Scheduling and Optimization of Electric Buses in a Depot," Energies, MDPI, vol. 15(14), pages 1-18, July.
    2. Kristián Čulík & Vladimíra Štefancová & Karol Hrudkay & Ján Morgoš, 2021. "Interior Heating and Its Influence on Electric Bus Consumption," Energies, MDPI, vol. 14(24), pages 1-19, December.
    3. Orhan Topal & İsmail Nakir, 2018. "Total Cost of Ownership Based Economic Analysis of Diesel, CNG and Electric Bus Concepts for the Public Transport in Istanbul City," Energies, MDPI, vol. 11(9), pages 1-17, September.
    4. Izabela Zoltowska & Jeremy Lin, 2021. "Optimal Charging Schedule Planning for Electric Buses Using Aggregated Day-Ahead Auction Bids," Energies, MDPI, vol. 14(16), pages 1-18, August.
    5. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    6. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    7. Papa, Gregor & Santo Zarnik, Marina & Vukašinović, Vida, 2022. "Electric-bus routes in hilly urban areas: Overview and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    8. Eckard Helmers & Johannes Dietz & Martin Weiss, 2020. "Sensitivity Analysis in the Life-Cycle Assessment of Electric vs. Combustion Engine Cars under Approximate Real-World Conditions," Sustainability, MDPI, vol. 12(3), pages 1-31, February.
    9. Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
    10. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    11. Adnane Houbbadi & Rochdi Trigui & Serge Pelissier & Eduardo Redondo-Iglesias & Tanguy Bouton, 2019. "Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging," Energies, MDPI, vol. 12(14), pages 1-17, July.
    12. Junpeng Cai & Dewang Chen & Shixiong Jiang & Weijing Pan, 2020. "Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    13. Andrzej Łebkowski, 2019. "Studies of Energy Consumption by a City Bus Powered by a Hybrid Energy Storage System in Variable Road Conditions," Energies, MDPI, vol. 12(5), pages 1-39, March.
    14. Foda, Ahmed & Abdelaty, Hatem & Mohamed, Moataz & El-Saadany, Ehab, 2023. "A generic cost-utility-emission optimization for electric bus transit infrastructure planning and charging scheduling," Energy, Elsevier, vol. 277(C).
    15. Zeng, Ziling & Wang, Shuaian & Qu, Xiaobo, 2022. "On the role of battery degradation in en-route charge scheduling for an electric bus system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    16. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    17. Jean-Michel Clairand & Javier Rodríguez-García & Carlos Álvarez-Bel, 2018. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation," Energies, MDPI, vol. 11(11), pages 1-21, November.
    18. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    19. Brinkel, Nico & Zijlstra, Marle & van Bezu, Ronald & van Twuijver, Tim & Lampropoulos, Ioannis & van Sark, Wilfried, 2023. "A comparative analysis of charging strategies for battery electric buses in wholesale electricity and ancillary services markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    20. Teresa Pamuła & Wiesław Pamuła, 2020. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning," Energies, MDPI, vol. 13(9), pages 1-17, May.
    21. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    22. Feifeng Zheng & Zhaojie Wang & Ming Liu, 2022. "Overnight charging scheduling of battery electric buses with uncertain charging time," Operational Research, Springer, vol. 22(5), pages 4865-4903, November.
    23. Cong, Yuan & Wang, Heqi & Bie, Yiming & Wu, Jiabin, 2023. "Double-battery configuration method for electric bus operation in cold regions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    2. Balali, Yasaman & Stegen, Sascha, 2021. "Review of energy storage systems for vehicles based on technology, environmental impacts, and costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    4. Lee, Dong-Yeon & Elgowainy, Amgad & Vijayagopal, Ram, 2019. "Well-to-wheel environmental implications of fuel economy targets for hydrogen fuel cell electric buses in the United States," Energy Policy, Elsevier, vol. 128(C), pages 565-583.
    5. Yajing Gao & Xiaojie Zhou & Jiafeng Ren & Zheng Zhao & Fushen Xue, 2018. "Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game," Energies, MDPI, vol. 11(5), pages 1-19, April.
    6. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    7. Alves, Luís & Pereira, Vítor & Lagarteira, Tiago & Mendes, Adélio, 2021. "Catalytic methane decomposition to boost the energy transition: Scientific and technological advancements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    8. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    9. Sofia Dahlgren & Jonas Ammenberg, 2021. "Sustainability Assessment of Public Transport, Part II—Applying a Multi-Criteria Assessment Method to Compare Different Bus Technologies," Sustainability, MDPI, vol. 13(3), pages 1-30, January.
    10. István Árpád & Judit T. Kiss & Gábor Bellér & Dénes Kocsis, 2021. "Sustainability Investigation of Vehicles’ CO 2 Emission in Hungary," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    11. Paulo J. G. Ribeiro & José F. G. Mendes, 2022. "Towards Zero CO 2 Emissions from Public Transport: The Pathway to the Decarbonization of the Portuguese Urban Bus Fleet," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    12. Imran, Muhammad & Haglind, Fredrik & Asim, Muhammad & Zeb Alvi, Jahan, 2018. "Recent research trends in organic Rankine cycle technology: A bibliometric approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 552-562.
    13. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    14. Shaohua Cui & Hui Zhao & Cuiping Zhang, 2018. "Locating Charging Stations of Various Sizes with Different Numbers of Chargers for Battery Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-22, November.
    15. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    16. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    17. Seyed Mahmoud Zanjirchi & Mina Rezaeian Abrishami & Negar Jalilian, 2019. "Four decades of fuzzy sets theory in operations management: application of life-cycle, bibliometrics and content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1289-1309, June.
    18. Hang Yin & Zeyu Wu & Junchao Wu & Junjie Jiang & Yalin Chen & Mingxuan Chen & Shixuan Luo & Lijun Gao, 2023. "A Hybrid Medium and Long-Term Relative Humidity Point and Interval Prediction Method for Intensive Poultry Farming," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    19. Yian Yan & Huang Wang & Jiuchun Jiang & Weige Zhang & Yan Bao & Mei Huang, 2019. "Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station," Energies, MDPI, vol. 12(3), pages 1-17, February.
    20. He, Yi & Liu, Zhaocai & Song, Ziqi, 2020. "Optimal charging scheduling and management for a fast-charging battery electric bus system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2060-:d:162604. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.