IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v243y2022ics0360544221030012.html
   My bibliography  Save this article

Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition

Author

Listed:
  • Shi, Junzhe
  • Xu, Bin
  • Shen, Yimin
  • Wu, Jingbo

Abstract

The rapid development of cloud techniques like Vehicle-to-Cloud (V2C) makes it possible to gather more information and develop computationally efficient energy management systems (EMS) for electric vehicles. This paper proposes a novel EMS with low computational cost targeting hybrid battery/ultracapacitor electric buses to reduce energy consumption and battery life degradation. In the offline training process, by applying the K-means clustering method with 10 selected features, 16 typical driving conditions are classified. For each driving condition, dynamic programming is employed offline to generate global optimal results, which are then used in control rule extraction for online operation. During the online operation, the proposed EMS executes the designed driving pattern recognition algorithm with V2C assistance to select optimal control rules. The simulation results indicate that the proposed EMS effectively decreases the battery degradation and energy consumption cost by 13.89%, compared with the conventional rule-based strategy. In addition, it is shown that V2C assistance leads to a 6.81% lower cost. Besides, the robustness of the proposed EMS is validated by testing the EMS with highly randomized input with uncertainties up to 15% and long duration of V2C data packet loss up to 10 s.

Suggested Citation

  • Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221030012
    DOI: 10.1016/j.energy.2021.122752
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221030012
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122752?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    2. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Han, Xuebing & Ouyang, Minggao, 2014. "Energy management strategies comparison for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 134(C), pages 321-331.
    3. Guo, Hongqiang & Hou, Daizheng & Du, Shangye & Zhao, Ling & Wu, Jian & Yan, Ning, 2020. "A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass," Energy, Elsevier, vol. 198(C).
    4. Song, Ziyou & Hou, Jun & Xu, Shaobing & Ouyang, Minggao & Li, Jianqiu, 2017. "The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses," Energy, Elsevier, vol. 135(C), pages 91-100.
    5. Zeng, Tao & Zhang, Caizhi & Hao, Dong & Cao, Dongpu & Chen, Jiawei & Chen, Jinrui & Li, Jin, 2020. "Data-driven approach for short-term power demand prediction of fuel cell hybrid vehicles," Energy, Elsevier, vol. 208(C).
    6. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    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. Wang, Bin & Wang, Chaohui & Wang, Zhiyu & Ni, Siliang & Yang, Yixin & Tian, Pengyu, 2023. "Adaptive state of energy evaluation for supercapacitor in emergency power system of more-electric aircraft," Energy, Elsevier, vol. 263(PA).
    2. Zong, Fang & Li, Yu-Xuan & Zeng, Meng, 2023. "Developing a carbon emission charging scheme considering mobility as a service," Energy, Elsevier, vol. 267(C).
    3. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    4. Shi, Junzhe & Zeng, Teng & Moura, Scott, 2023. "Electric fleet charging management considering battery degradation and nonlinear charging profile," Energy, Elsevier, vol. 283(C).
    5. Eiman ElGhanam & Hazem Sharf & Mohamed S. Hassan & Ahmed Osman, 2023. "Performance Evaluation of Hybrid Battery–Supercapacitor-Based Energy Storage Systems for Urban-Driven Electric Vehicles," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    6. Liu, Huimin & Lin, Cheng & Yu, Xiao & Tao, Zhenyi & Xu, Jiaqi, 2024. "Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle," Applied Energy, Elsevier, vol. 365(C).
    7. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    8. Yu, Xiao & Lin, Cheng & Xie, Peng & Liang, Sheng, 2022. "A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 256(C).
    9. Momcilovic, Vladimir & Dimitrijevic, Branka & Stokic, Marko, 2023. "Supercapacitor electric bus modeling and simulation framework," Energy, Elsevier, vol. 282(C).
    10. He, Hongwen & Su, Qicong & Huang, Ruchen & Niu, Zegong, 2024. "Enabling intelligent transferable energy management of series hybrid electric tracked vehicle across motion dimensions via soft actor-critic algorithm," Energy, Elsevier, vol. 294(C).
    11. Shiyou Tao & Zhaohui Peng & Weiguang Zheng, 2024. "Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules," Sustainability, MDPI, vol. 16(17), pages 1-22, August.
    12. Xue, Jiaqi & Jiao, Xiaohong & Yu, Danmei & Zhang, Yahui, 2023. "Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
    13. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    14. Zhaowen Liang & Kai Liu & Jinjin Huang & Enfei Zhou & Chao Wang & Hui Wang & Qiong Huang & Zhenpo Wang, 2022. "Powertrain Design and Energy Management Strategy Optimization for a Fuel Cell Electric Intercity Coach in an Extremely Cold Mountain Area," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    15. Yu, Xiao & Lin, Cheng & Tian, Yu & Zhao, Mingjie & Liu, Huimin & Xie, Peng & Zhang, JunZhi, 2023. "Real-time and hierarchical energy management-control framework for electric vehicles with dual-motor powertrain system," Energy, Elsevier, vol. 272(C).
    16. Menglin Li & Haoran Liu & Mei Yan & Hongyang Xu & Hongwen He, 2022. "A Novel Multi-Objective Energy Management Strategy for Fuel Cell Buses Quantifying Fuel Cell Degradation as Operating Cost," Sustainability, MDPI, vol. 14(23), pages 1-16, December.

    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. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
    2. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    3. Song, Ziyou & Li, Jianqiu & Hou, Jun & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study," Energy, Elsevier, vol. 154(C), pages 433-441.
    4. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    5. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    6. Ghosh, Sourav & Yadav, Sarita & Devi, Ambika & Thomas, Tiju, 2022. "Techno-economic understanding of Indian energy-storage market: A perspective on green materials-based supercapacitor technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    8. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    9. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    10. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    11. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    12. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    13. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    14. Yang, Bo & Zhu, Tianjiao & Zhang, Xiaoshun & Wang, Jingbo & Shu, Hongchun & Li, Shengnan & He, Tingyi & Yang, Lei & Yu, Tao, 2020. "Design and implementation of Battery/SMES hybrid energy storage systems used in electric vehicles: A nonlinear robust fractional-order control approach," Energy, Elsevier, vol. 191(C).
    15. Laird, Cary & Kang, Ziliang & James, Kai A. & Alleyne, Andrew G., 2022. "Framework for integrated plant and control optimization of electro-thermal systems: An energy storage system case study," Energy, Elsevier, vol. 258(C).
    16. Li, Jianwei & Wang, Xudong & Zhang, Zhenyu & Le Blond, Simon & Yang, Qingqing & Zhang, Min & Yuan, Weijia, 2017. "Analysis of a new design of the hybrid energy storage system used in the residential m-CHP systems," Applied Energy, Elsevier, vol. 187(C), pages 169-179.
    17. Herrera, Victor & Milo, Aitor & Gaztañaga, Haizea & Etxeberria-Otadui, Ion & Villarreal, Igor & Camblong, Haritza, 2016. "Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway," Applied Energy, Elsevier, vol. 169(C), pages 831-845.
    18. Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.
    19. Zhang, Feitie & Yang, Fuyuan & Xue, Dianlun & Cai, Yuanchun, 2019. "Optimization of compound power split configurations in PHEV bus for fuel consumption and battery degradation decreasing," Energy, Elsevier, vol. 169(C), pages 937-957.
    20. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(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:eee:energy:v:243:y:2022:i:c:s0360544221030012. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.