IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v256y2022ics0360544222015857.html
   My bibliography  Save this article

Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain

Author

Listed:
  • Bao, Shuyue
  • Sun, Ping
  • Zhu, Jianxin
  • Ji, Qian
  • Liu, Junheng

Abstract

Dynamic programming is a widely used algorithm to solve the optimal control problem for hybrid electric vehicle in the whole driving scenario, but the problems of interpolation error and dimensional disaster have not been completely solved. This study proposes an adaptive adjustment method to solve the interpolation error problem, in which the solution without theoretical error can be obtained at the cost of a tiny driving cycle accuracy. Furthermore, a dynamic equivalent consumption factor calculation method is proposed to analyze the change of energy storage quality in the battery pack and the instantaneous equivalent consumption of the motors. The effectiveness of the improved algorithm is verified on a passenger car with hybrid powertrain using planetary gear mechanism. The results show that the established multi-dimensional dynamic programming algorithm has a relatively stable energy-saving ability. The maximum gap of fuel consumption rate is 3.2% in the four test driving cycles. Besides, there is only a maximum difference of 0.29‱ between the original driving cycles and the new cycles generated by the adaptive adjustment method. And obvious dynamic equivalent consumption factor changes of 15.7% in WLTC-class3, 10.3% in CLTC-P, 11.8% in FTP75 and 11.0% in NEDC are observed through the proposed calculation method.

Suggested Citation

  • Bao, Shuyue & Sun, Ping & Zhu, Jianxin & Ji, Qian & Liu, Junheng, 2022. "Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015857
    DOI: 10.1016/j.energy.2022.124682
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222015857
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124682?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. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    2. Li, Maobing & Xu, Hui & Li, Weimin & Liu, Yin & Li, Fade & Hu, Yue & Liu, Li, 2016. "The structure and control method of hybrid power source for electric vehicle," Energy, Elsevier, vol. 112(C), pages 1273-1285.
    3. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    4. Chen, Syuan-Yi & Wu, Chien-Hsun & Hung, Yi-Hsuan & Chung, Cheng-Ta, 2018. "Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization," Energy, Elsevier, vol. 160(C), pages 154-170.
    5. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Total cost of ownership, payback, and consumer preference modeling of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 103(C), pages 488-506.
    6. Roberto Finesso & Daniela Misul & Ezio Spessa & Mattia Venditti, 2018. "Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO 2 Emission Minimization," Energies, MDPI, vol. 11(7), pages 1-28, July.
    7. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine," Applied Energy, Elsevier, vol. 254(C).
    8. Yang, Yalian & Pei, Huanxin & Hu, Xiaosong & Liu, Yonggang & Hou, Cong & Cao, Dongpu, 2019. "Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach," Energy, Elsevier, vol. 166(C), pages 929-938.
    9. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    10. Pérez, Laura V. & Bossio, Guillermo R. & Moitre, Diego & García, Guillermo O., 2006. "Optimization of power management in an hybrid electric vehicle using dynamic programming," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 244-254.
    11. 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.
    12. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
    13. Aghaei, Jamshid & Nezhad, Ali Esmaeel & Rabiee, Abdorreza & Rahimi, Ehsan, 2016. "Contribution of Plug-in Hybrid Electric Vehicles in power system uncertainty management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 450-458.
    14. Biswas, Dhrupad & Ghosh, Susenjit & Sengupta, Somnath & Mukhopadhyay, Siddhartha, 2022. "Energy Management of a Parallel Hybrid Electric Vehicle using Model Predictive Static Programming," Energy, Elsevier, vol. 250(C).
    15. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    16. Wu, Xiaolan & Cao, Binggang & Li, Xueyan & Xu, Jun & Ren, Xiaolong, 2011. "Component sizing optimization of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 88(3), pages 799-804, March.
    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. Yang, Kun & Zhang, Benjun & Chu, Yongkun & Wang, Zhongwei & Shao, Changjiang & Ma, Chao, 2024. "Research on the configuration design and energy management of a novel plug-in hybrid electric vehicle based on the double-rotor motor and hybrid energy storage system," Energy, Elsevier, vol. 302(C).
    2. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.
    3. Wei, Zhengchao & Ma, Yue & Yang, Ningkang & Ruan, Shumin & Xiang, Changle, 2023. "Reinforcement learning based power management integrating economic rotational speed of turboshaft engine and safety constraints of battery for hybrid electric power system," Energy, Elsevier, vol. 263(PB).
    4. Bao, Shuyue & Tang, Shifa & Sun, Ping & Wang, Tao, 2023. "LSTM-based energy management algorithm for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 284(C).
    5. Feng, Zhiyan & Zhang, Qingang & Zhang, Yiming & Fei, Liangyu & Jiang, Fei & Zhao, Shengdun, 2024. "Practicability analysis of online deep reinforcement learning towards energy management strategy of 4WD-BEVs driven by dual-motor in-wheel motors," Energy, Elsevier, vol. 290(C).
    6. Kunang Li & Chunchun Jia & Xuefeng Han & Hongwen He, 2023. "A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    7. Zhang, Yahui & Wei, Zeyi & Wang, Zhong & Tian, Yang & Wang, Jizhe & Tian, Zhikun & Xu, Fuguo & Jiao, Xiaohong & Li, Liang & Wen, Guilin, 2024. "Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation," Energy, Elsevier, vol. 292(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. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
    2. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Deng, Pengyi & Wu, Xiaohua & Zhang, Jiye, 2022. "Hierarchical energy management of a hybrid propulsion system considering speed profile optimization," Energy, Elsevier, vol. 244(PB).
    3. Anselma, Pier Giuseppe, 2022. "Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios," Applied Energy, Elsevier, vol. 314(C).
    4. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. 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).
    6. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2020. "Comparison of four-wheel-drive hybrid powertrain configurations," Energy, Elsevier, vol. 209(C).
    7. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    8. Liu, Xinzhi & Qi, Nanjian & Dai, Keren & Yin, Yajiang & Zhao, Jiahao & Wang, Xiaofeng & You, Zheng, 2022. "Sponge Supercapacitor rule-based energy management strategy for wireless sensor nodes optimized by using dynamic programing algorithm," Energy, Elsevier, vol. 239(PE).
    9. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    10. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    11. 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).
    12. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    13. Zhou, Jianhao & Xue, Yuan & Xu, Da & Li, Chaoxiong & Zhao, Wanzhong, 2022. "Self-learning energy management strategy for hybrid electric vehicle via curiosity-inspired asynchronous deep reinforcement learning," Energy, Elsevier, vol. 242(C).
    14. Mayyas, Abdel Ra'ouf & Kumar, Sushil & Pisu, Pierluigi & Rios, Jacqueline & Jethani, Puneet, 2017. "Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach," Applied Energy, Elsevier, vol. 204(C), pages 287-302.
    15. Chaoying Xia & Cong Zhang, 2015. "Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index," Energies, MDPI, vol. 8(11), pages 1-16, November.
    16. 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).
    17. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2016. "Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios," Applied Energy, Elsevier, vol. 177(C), pages 366-383.
    18. 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.
    19. Gaizka Saldaña & Jose Ignacio San Martin & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation," Energies, MDPI, vol. 12(12), pages 1-37, June.
    20. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(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:256:y:2022:i:c:s0360544222015857. 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.