IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v185y2017ip2p1633-1643.html
   My bibliography  Save this article

Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming

Author

Listed:
  • Peng, Jiankun
  • He, Hongwen
  • Xiong, Rui

Abstract

An appropriate energy management strategy is able to further improve the fuel economy of PHEVs. The rule-based energy management algorithms are dominated in industry due to their fast computation and ease of establishment potentials, however, their performance differ a lot from improper setting of parameters and control actions. This paper employs the dynamic programming (DP) to locate the optimal actions for the engine in PHEVs, and more importantly, proposes a recalibration method to improve the performance of the rule-based energy management through the results calculated by DP algorithm. Eventually, an optimization-based rule development procedure is presented and further validated by hardware-in-loop (HIL) simulation experiments. The HIL simulation results show that, the improved rule-based energy management strategy reduces fuel consumption per 100km from 25.46L diesel to 22.80L diesel. The main contribution of this study is to explore a novel way to calibrate the existed heuristic control strategy with the global optimization result through advanced intelligent algorithms.

Suggested Citation

  • Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1633-1643
    DOI: 10.1016/j.apenergy.2015.12.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915016049
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.12.031?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. Jiankun Peng & Hao Fan & Hongwen He & Deng Pan, 2015. "A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus," Energies, MDPI, vol. 8(6), pages 1-21, June.
    2. Zhang, Shuo & Xiong, Rui & Zhang, Chengning, 2015. "Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus," Applied Energy, Elsevier, vol. 159(C), pages 370-380.
    3. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    4. 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.
    5. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    6. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    7. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    8. Ximing Wang & Hongwen He & Fengchun Sun & Xiaokun Sun & Henglu Tang, 2013. "Comparative Study on Different Energy Management Strategies for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 6(11), pages 1-20, October.
    9. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    10. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    Full references (including those not matched with items on IDEAS)

    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. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    2. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    3. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    4. 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).
    5. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    6. Qicheng Xue & Xin Zhang & Teng Teng & Jibao Zhang & Zhiyuan Feng & Qinyang Lv, 2020. "A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-30, October.
    7. Li, Gaopeng & Zhang, Jieli & He, Hongwen, 2017. "Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 194(C), pages 578-587.
    8. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    9. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    10. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2015. "A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs," Applied Energy, Elsevier, vol. 139(C), pages 260-271.
    11. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    12. Yongpeng Shen & Zhendong He & Dongqi Liu & Binjie Xu, 2016. "Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model," Energies, MDPI, vol. 9(2), pages 1-18, February.
    13. 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.
    14. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
    15. Liu, Hui & Li, Xunming & Wang, Weida & Han, Lijin & Xiang, Changle, 2018. "Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 427-444.
    16. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
    17. Kegang Zhao & Jinghao Bei & Yanwei Liu & Zhihao Liang, 2019. "Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method," Energies, MDPI, vol. 12(17), pages 1-23, August.
    18. 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).
    19. Xu, Nan & Kong, Yan & Zhang, Yuanjian & Yue, Fenglai & Sui, Yan & Li, Xiaohan & Liu, Heng & Xu, Zhe, 2022. "Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy," Energy, Elsevier, vol. 251(C).
    20. 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.

    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:185:y:2017:i:p2:p:1633-1643. 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.