IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v175y2019icp378-392.html
   My bibliography  Save this article

A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles

Author

Listed:
  • Jinquan, Guo
  • Hongwen, He
  • Jiankun, Peng
  • Nana, Zhou

Abstract

In this paper, an adaptive energy management strategy (AEMS) under model predictive control (MPC) framework is proposed. The main advantage of the AEMS is that it fully integrates the economy driving pro system (EDPS), which can provide the renewable energy consumption trajectory considering dynamic traffic information of target driving task, namely the state of charge (SOC) reference constraint for the MPC optimal calculation at each control step. Moreover, based on the dynamically updated traffic information, the SOC reference constraint will be re-planned with correction, which will further reflect the ideal energy consumption trend over the actual driving cycle. For the MPC prediction aspect, the deep neural network (DNN) is applied in this paper to predict the future short-term velocity with 5s, 10s and 15s horizon, respectively. Meanwhile, the dynamic programming (DP) is applied to calculate the optimal energy distribution at each MPC control step. Simulation results show that under the test driving cycle, the optimal MPC predictive horizon with the assistance of EDPS is 10s, and the fuel economy rate can improve up to 6.48% compared with energy management without the assistance of EDPS. Moreover, the HIL test indicates the AEMS has well real-time performance as well.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:175:y:2019:i:c:p:378-392
    DOI: 10.1016/j.energy.2019.03.083
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219304876
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.03.083?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. Sun, Huaping & Geng, Yong & Hu, Lingxiang & Shi, Longyu & Xu, Tong, 2018. "Measuring China's new energy vehicle patents: A social network analysis approach," Energy, Elsevier, vol. 153(C), pages 685-693.
    2. 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.
    3. 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.
    4. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    5. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    6. Shi, Dehua & Pisu, Pierluigi & Chen, Long & Wang, Shaohua & Wang, Renguang, 2016. "Control design and fuel economy investigation of power split HEV with energy regeneration of suspension," Applied Energy, Elsevier, vol. 182(C), pages 576-589.
    7. Soares M.C. Borba, Bruno & Szklo, Alexandre & Schaeffer, Roberto, 2012. "Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil," Energy, Elsevier, vol. 37(1), pages 469-481.
    8. Škugor, Branimir & Deur, Joško, 2015. "Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model," Energy, Elsevier, vol. 92(P3), pages 456-465.
    9. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    10. Yonggang Liu & Jie Li & Ming Ye & Datong Qin & Yi Zhang & Zhenzhen Lei, 2017. "Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information," Energies, MDPI, vol. 10(4), pages 1-20, March.
    11. 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. Sun, Xilei & Fu, Jianqin & Yang, Huiyong & Xie, Mingke & Liu, Jingping, 2023. "An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control," Energy, Elsevier, vol. 269(C).
    2. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    3. Zhang, Yuanjian & Chu, Liang & Fu, Zicheng & Xu, Nan & Guo, Chong & Zhao, Di & Ou, Yang & Xu, Lei, 2020. "Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control," Energy, Elsevier, vol. 197(C).
    4. 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).
    5. 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).
    6. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    7. 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.
    8. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2022. "Driving information process system-based real-time energy management for the fuel cell bus to minimize fuel cell engine aging and energy consumption," Energy, Elsevier, vol. 248(C).
    9. 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).
    10. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    11. Wu, Changcheng & Ruan, Jiageng & Cui, Hanghang & Zhang, Bin & Li, Tongyang & Zhang, Kaixuan, 2023. "The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle, part I: Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid ," Energy, Elsevier, vol. 262(PB).
    12. Wang, Hanchen & Ye, Yiming & Zhang, Jiangfeng & Xu, Bin, 2023. "A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle," Energy, Elsevier, vol. 266(C).
    13. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    14. 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.
    15. 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).
    16. Lin Li & Serdar Coskun & Jiaze Wang & Youming Fan & Fengqi Zhang & Reza Langari, 2021. "Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples," Energies, MDPI, vol. 14(12), pages 1-30, June.
    17. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    18. 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).
    19. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    20. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    21. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    22. 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).
    23. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    24. Yuan, Jingni & Yang, Lin, 2019. "Predictive energy management strategy for connected 48V hybrid electric vehicles," Energy, Elsevier, vol. 187(C).
    25. Babar, Abdul Haseeb Khan & Ali, Yousaf, 2021. "Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment," Technological Forecasting and Social Change, Elsevier, vol. 167(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. 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).
    2. 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.
    3. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
    4. Shaobo Xie & Huiling Li & Zongke Xin & Tong Liu & Lang Wei, 2017. "A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route," Energies, MDPI, vol. 10(9), pages 1-22, September.
    5. Guo, Hongqiang & Lu, Silong & Hui, Hongzhong & Bao, Chunjiang & Shangguan, Jinyong, 2019. "Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass," Energy, Elsevier, vol. 176(C), pages 292-308.
    6. Poullikkas, Andreas, 2015. "Sustainable options for electric vehicle technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1277-1287.
    7. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    8. Millo, F. & Cubito, C. & Rolando, L. & Pautasso, E. & Servetto, E., 2017. "Design and development of an hybrid light commercial vehicle," Energy, Elsevier, vol. 136(C), pages 90-99.
    9. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    10. Jichao Liu & Yanyan Liang & Zheng Chen & Hai Yang, 2023. "An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks," Energies, MDPI, vol. 16(9), pages 1-26, May.
    11. 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.
    12. Lin, Cheng & Zhao, Mingjie & Pan, Hong & Yi, Jiang, 2019. "Blending gear shift strategy design and comparison study for a battery electric city bus with AMT," Energy, Elsevier, vol. 185(C), pages 1-14.
    13. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    14. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.
    15. Lin, Xinyou & Xia, Yutian & Huang, Wei & Li, Hailin, 2021. "Trip distance adaptive power prediction control strategy optimization for a Plug-in Fuel Cell Electric Vehicle," Energy, Elsevier, vol. 224(C).
    16. Ju, Fei & Zhuang, Weichao & Wang, Liangmo & Zhang, Zhe, 2019. "Optimal sizing and adaptive energy management of a novel four-wheel-drive hybrid powertrain," Energy, Elsevier, vol. 187(C).
    17. Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
    18. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    19. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
    20. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(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:175:y:2019:i:c:p:378-392. 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.