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SDP Policy Iteration-Based Energy Management Strategy Using Traffic Information for Commuter Hybrid Electric Vehicles

Author

Listed:
  • Xiaohong Jiao

    (Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Tielong Shen

    (Department of Engineering and Applied Sciences, Sophia University, Tokyo 102-8554, Japan)

Abstract

This paper demonstrates an energy management method using traffic information for commuter hybrid electric vehicles. A control strategy based on stochastic dynamic programming (SDP) is developed, which minimizes on average the equivalent fuel consumption, while satisfying the battery charge-sustaining constraints and the overall vehicle power demand for drivability. First, according to the sample information of the traffic speed profiles, the regular route is divided into several segments and the statistic characteristics in the different segments are constructed from gathered data on the averaged vehicle speeds. And then, the energy management problem is formulated as a stochastic nonlinear and constrained optimal control problem and a modified policy iteration algorithm is utilized to generate a time-invariant state-dependent power split strategy. Finally, simulation results over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.

Suggested Citation

  • Xiaohong Jiao & Tielong Shen, 2014. "SDP Policy Iteration-Based Energy Management Strategy Using Traffic Information for Commuter Hybrid Electric Vehicles," Energies, MDPI, vol. 7(7), pages 1-28, July.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:7:p:4648-4675:d:38437
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

    1. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    2. Zou, Runnan & Fan, Likang & Dong, Yanrui & Zheng, Siyu & Hu, Chenxing, 2021. "DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle," Energy, Elsevier, vol. 225(C).
    3. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    4. Shi, Dehua & Xu, Han & Wang, Shaohua & Hu, Jia & Chen, Long & Yin, Chunfang, 2024. "Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network," Energy, Elsevier, vol. 305(C).
    5. Tang, Wenbin & Wang, Yaqian & Jiao, Xiaohong & Ren, Lina, 2023. "Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios," Energy, Elsevier, vol. 265(C).
    6. Yuying Wang & Xiaohong Jiao & Zitao Sun & Ping Li, 2017. "Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 10(11), pages 1-21, November.

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