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Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Author

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  • Li, Ji
  • Zhou, Quan
  • He, Yinglong
  • Shuai, Bin
  • Li, Ziyang
  • Williams, Huw
  • Xu, Hongming

Abstract

This paper investigates an online predictive control strategy for series-parallel plug-in hybrid electric vehicles (PHEVs), resulting in a novel online optimization methodology named the dual-loop online intelligent programming (DOIP) that is proposed for velocity prediction and energy-flow control. By reconsidering the change of driving behaviours at each look-ahead step, this methodology guarantees the effectiveness of optimal control sequence in the energy-saving efficiency of online predictive energy management. The design procedure starts with the simulation of a series-parallel PHEV using a systematic control-oriented model and the definition of a cost function. Inspired by fuzzy granulation technology, a deep fuzzy predictor is created to achieve driver-oriented velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities between vehicle speed and acceleration. To determine the optimal control behaviours and power distribution between two energy sources, chaos-enhanced accelerated swarm optimization is developed for the DOIP algorithm. The prediction capability of the deep fuzzy predictor is evaluated by comparing with two existing predictors over the WLTP-based driving cycle. The proposed control strategy is contrasted with short-sighted and dynamic programming based counterparts, and validated by a driver-in-the-loop test. The results demonstrate that the deep fuzzy predictor can effectively recognize driving behaviour and reduce at least 19% errors compared to involved Markov chain based predictors. Online predictive control strategy using the DOIP algorithm is able to significantly reduce 9.37% fuel consumption from the baseline and shorten computational time.

Suggested Citation

  • Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:60
    DOI: 10.1016/j.apenergy.2019.113617
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    4. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    5. 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.
    6. 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.
    7. Chen, Zeyu & Xiong, Rui & Wang, Chun & Cao, Jiayi, 2017. "An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle," Applied Energy, Elsevier, vol. 185(P2), pages 1663-1672.
    8. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    9. Liu, Teng & Wang, Bo & Yang, Chenglang, 2018. "Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning," Energy, Elsevier, vol. 160(C), pages 544-555.
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    7. Zhang, Caizhi & Zeng, Tao & Wu, Qi & Deng, Chenghao & Chan, Siew Hwa & Liu, Zhixiang, 2021. "Improved efficiency maximization strategy for vehicular dual-stack fuel cell system considering load state of sub-stacks through predictive soft-loading," Renewable Energy, Elsevier, vol. 179(C), pages 929-944.
    8. Guille des Buttes, Alice & Jeanneret, Bruno & Kéromnès, Alan & Le Moyne, Luis & Pélissier, Serge, 2020. "Energy management strategy to reduce pollutant emissions during the catalyst light-off of parallel hybrid vehicles," Applied Energy, Elsevier, vol. 266(C).
    9. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    10. 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.
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