IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v225y2021ics0360544221005223.html
   My bibliography  Save this article

An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications

Author

Listed:
  • He, Hongwen
  • Wang, Yunlong
  • Han, Ruoyan
  • Han, Mo
  • Bai, Yunfei
  • Liu, Qingwu

Abstract

The energy management strategy (EMS) and its real-time adjustment ability accordingly influence a lot on the fuel economy of a hybrid electric vehicle. This paper proposes an improved model predictive control (MPC) framework for the EMS of plug-in hybrid electric buses (PHEB). It aims to achieve optimal energy distribution with increased prediction accuracy and optimized speed sequences by integrating the V2V and V2I information. Firstly, when PHEB is driving between two traffic intersections, the speed prediction accuracy is improved with the Particle Swarm Optimization (PSO) method optimizing the initial value of the Extreme Learning Machine (ELM). Based on the information from V2V, the instantaneous safe speed is calculated and used as a reference to update the predicted speed and reduce speed fluctuations. Secondly, when passing through a traffic intersection, the optimal speed sequence is planned in advance by the dynamic planning algorithm, with the PHEB’s state at the traffic intersection is predicted based on the current signal state (red-yellow-green). Finally, combining speed prediction and speed planning with rolling optimization and feedback correction, MPC-based optimal energy management is achieved. The experimental results show that under the new MPC framework, fuel consumption is reduced by 13.55% relative to the rule-based strategy.

Suggested Citation

  • He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221005223
    DOI: 10.1016/j.energy.2021.120273
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221005223
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120273?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. Wei, Zhen & Xu, John & Halim, Dunant, 2017. "HEV power management control strategy for urban driving," Applied Energy, Elsevier, vol. 194(C), pages 705-714.
    2. Fathabadi, Hassan, 2015. "Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems," Energy, Elsevier, vol. 90(P1), pages 1100-1110.
    3. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    4. Ziying Yang & Manping Tang, 2019. "Welfare Analysis of Government Subsidy Programs for Fuel-Efficient Vehicles and New Energy Vehicles in China," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(2), pages 911-937, October.
    5. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
    6. Du, Jiuyu & Ouyang, Minggao & Chen, Jingfu, 2017. "Prospects for Chinese electric vehicle technologies in 2016–2020: Ambition and rationality," Energy, Elsevier, vol. 120(C), pages 584-596.
    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. Xiaodong Liu & Hongqiang Guo & Xingqun Cheng & Juan Du & Jian Ma, 2022. "A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 15(20), pages 1-24, October.
    2. Wang, Yanxia & Gan, Shaojun & Li, Kang & Chen, Yanyan, 2022. "Planning for low-carbon energy-transportation system at metropolitan scale: A case study of Beijing, China," Energy, Elsevier, vol. 246(C).
    3. Gil-Sayas, Susana & Komnos, Dimitrios & Lodi, Chiara & Currò, Davide & Serra, Simone & Broatch, Alberto & Fontaras, Georgios, 2022. "Analysing the potential of a simulation-based method for the assessment of CO2 savings from eco-innovative technologies in light-duty vehicles," Energy, Elsevier, vol. 245(C).
    4. Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
    5. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
    6. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    7. Li, Kai & Chen, Hong & Hou, Shengyan & Eriksson, Lars & Gao, Jinwu, 2023. "A novel engine and battery coupled thermal management strategy for connected HEVs based on switched model predictive control under low temperature," Energy, Elsevier, vol. 278(C).
    8. Jiang, Yue & Meng, Hao & Chen, Guanpeng & Yang, Congnan & Xu, Xiaojun & Zhang, Lei & Xu, Haijun, 2022. "Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle," Energy, Elsevier, vol. 254(PA).
    9. Alessia Musa & Michele Pipicelli & Matteo Spano & Francesco Tufano & Francesco De Nola & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul & Gianluca Toscano, 2021. "A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    10. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    11. Chen, Zheng & Wu, Simin & Shen, Shiquan & Liu, Yonggang & Guo, Fengxiang & Zhang, Yuanjian, 2023. "Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios," Energy, Elsevier, vol. 263(PF).
    12. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    13. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Xiang, Changle & Li, Xunming, 2022. "Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach," Energy, Elsevier, vol. 247(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. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    2. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    3. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2021. "Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning," Energy, Elsevier, vol. 226(C).
    4. Hongxia Sun & Yao Wan & Huirong Lv, 2020. "System Dynamics Model for the Evolutionary Behaviour of Government Enterprises and Consumers in China’s New Energy Vehicle Market," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    5. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    6. Cui, Yu & Khan, Sufyan Ullah & Li, Zhixue & Zhao, Minjuan, 2021. "Environmental effect, price subsidy and financial performance: Evidence from Chinese new energy enterprises," Energy Policy, Elsevier, vol. 149(C).
    7. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    8. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    9. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Ekşioğlu, Sandra D. & Castillo-Villar, Krystel K., 2021. "Designing a reliable electric vehicle charging station expansion under uncertainty," International Journal of Production Economics, Elsevier, vol. 236(C).
    10. He, Wentao & Hao, Xiaoli, 2023. "Competition and welfare effects of introducing new products into the new energy vehicle market: Empirical evidence from Tesla’s entry into the Chinese market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    11. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    12. Jianlong Wu & Zhongji Yang & Xiaobo Hu & Hongqi Wang & Jing Huang, 2018. "Exploring Driving Forces of Sustainable Development of China’s New Energy Vehicle Industry: An Analysis from the Perspective of an Innovation Ecosystem," Sustainability, MDPI, vol. 10(12), pages 1-24, December.
    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. Codani, Paul & Perez, Yannick & Petit, Marc, 2016. "Financial shortfall for electric vehicles: Economic impacts of Transmission System Operators market designs," Energy, Elsevier, vol. 113(C), pages 422-431.
    15. Nunes, Pedro & Brito, M.C., 2017. "Displacing natural gas with electric vehicles for grid stabilization," Energy, Elsevier, vol. 141(C), pages 87-96.
    16. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    17. Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
    18. 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.
    19. 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.
    20. García-Vázquez, Carlos A. & Llorens-Iborra, Francisco & Fernández-Ramírez, Luis M. & Sánchez-Sainz, Higinio & Jurado, Francisco, 2017. "Comparative study of dynamic wireless charging of electric vehicles in motorway, highway and urban stretches," Energy, Elsevier, vol. 137(C), pages 42-57.

    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:225:y:2021:i:c:s0360544221005223. 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.