IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v194y2017icp588-595.html
   My bibliography  Save this article

A pseudospectral method for solving optimal control problem of a hybrid tracked vehicle

Author

Listed:
  • Wei, Shouyang
  • Zou, Yuan
  • Sun, Fengchun
  • Christopher, Onder

Abstract

This study explored the feasibility of using the Radau pseudospectral method (RPM) to optimize the energy management strategy for a hybrid tracked vehicle. The engine–generator set and the battery pack of the serial hybrid tracked vehicle were modeled and validated through the bench test. A DC-DC converter was equipped between the battery pack and the DC bus in this hybrid powertrain, which increased the flexibility of energy distribution between the engine–generator set and the battery. It was simplified as a voltage regulator in the hybrid powertrain model. The power demand during the vehicle operation was calculated according to the vehicle dynamics and driving schedules. The optimal control problem was formulated to minimize the fuel consumption through regulating the power distribution properly between the engine–generator set and battery pack during a typical driving schedule. The RPM was applied to transform the optimal control problem to a finite-dimensional constrained nonlinear programming problem. A comparison of the solutions from RPM and dynamic programming showed that the former offers the higher computation efficiency and better fuel economy.

Suggested Citation

  • Wei, Shouyang & Zou, Yuan & Sun, Fengchun & Christopher, Onder, 2017. "A pseudospectral method for solving optimal control problem of a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 194(C), pages 588-595.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:588-595
    DOI: 10.1016/j.apenergy.2016.07.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916309618
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.07.020?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. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    2. Yuan Zou & Fengchun Sun & Xiaosong Hu & Lino Guzzella & Huei Peng, 2012. "Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle," Energies, MDPI, vol. 5(11), pages 1-14, November.
    3. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    4. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    5. Pérez, Laura V. & Pilotta, Elvio A., 2009. "Optimal power split in a hybrid electric vehicle using direct transcription of an optimal control problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1959-1970.
    6. 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.
    7. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
    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. Zhou, Quan & Du, Changqing & Wu, Dongmei & Huang, Cheng & Yan, Fuwu, 2023. "A tolerant sequential correction predictive energy management strategy of hybrid electric vehicles with adaptive mesh discretization," Energy, Elsevier, vol. 274(C).
    2. Qin, Zhaobo & Luo, Yugong & Zhuang, Weichao & Pan, Ziheng & Li, Keqiang & Peng, Huei, 2018. "Simultaneous optimization of topology, control and size for multi-mode hybrid tracked vehicles," Applied Energy, Elsevier, vol. 212(C), pages 1627-1641.
    3. Zhang, Haoxiang & Wang, Feng & Lin, Zichang & Xu, Bing, 2023. "Optimization of speed trajectory for electric wheel loaders: Battery lifetime extension," Applied Energy, Elsevier, vol. 351(C).
    4. Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    5. Anwar, Hamza & Vishwanath, Aashrith & Ahmed, Qadeer & Chunodkar, Apurva, 2023. "Comprehensive energy footprint benchmarking of commercial electrified powertrains," Applied Energy, Elsevier, vol. 345(C).
    6. Hong Huang & Li Zhai & Zeda Wang, 2018. "A Power Coupling System for Electric Tracked Vehicles during High-Speed Steering with Optimization-Based Torque Distribution Control," Energies, MDPI, vol. 11(6), pages 1-17, June.

    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. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    2. Chaofeng Pan & Yanyan Liang & Long Chen & Liao Chen, 2019. "Optimal Control for Hybrid Energy Storage Electric Vehicle to Achieve Energy Saving Using Dynamic Programming Approach," Energies, MDPI, vol. 12(4), pages 1-19, February.
    3. Jiang, Hongliang & Xu, Liangfei & Li, Jianqiu & Hu, Zunyan & Ouyang, Minggao, 2019. "Energy management and component sizing for a fuel cell/battery/supercapacitor hybrid powertrain based on two-dimensional optimization algorithms," Energy, Elsevier, vol. 177(C), pages 386-396.
    4. 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.
    5. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    6. 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.
    7. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    9. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    10. Li, Liang & Li, Xujian & Wang, Xiangyu & Song, Jian & He, Kai & Li, Chenfeng, 2016. "Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking," Applied Energy, Elsevier, vol. 176(C), pages 125-137.
    11. Trovão, João P. & Silva, Mário A. & Antunes, Carlos Henggeler & Dubois, Maxime R., 2017. "Stability enhancement of the motor drive DC input voltage of an electric vehicle using on-board hybrid energy storage systems," Applied Energy, Elsevier, vol. 205(C), pages 244-259.
    12. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    13. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    14. 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.
    15. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    17. Zhuang, Weichao & Zhang, Xiaowu & Li, Daofei & Wang, Liangmo & Yin, Guodong, 2017. "Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle," Applied Energy, Elsevier, vol. 204(C), pages 476-488.
    18. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    19. 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).
    20. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," 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:appene:v:194:y:2017:i:c:p:588-595. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.