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A predictive controller for real-time energy management of plug-in hybrid electric vehicles

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  • Hassanzadeh, Mojtaba
  • Rahmani, Zahra

Abstract

Battery aging can degrade the energy efficiency of plug-in hybrid electric vehicles (PHEVs) significantly. This paper presents a novel intelligent real-time energy management strategy (EMS) for PHEVs, integrating battery life and fuel consumption optimization. The two-objective offline optimization problem is solved by the dynamic programming (DP) approach to obtain the globally optimal solutions. For the real-time implementation, a model predictive control (MPC) scheme is combined with an adaptive neuro-fuzzy inference system (ANFIS) model. DP carries out a receding-horizon optimization using future traffic information. Level-set functions are exploited within the DP algorithm to reduce numerical errors and decrease the computational effort of the baseline DP approach. Contrary to the real-time EMSs with a pre-determined state-of-charge (SOC) reference, an ANFIS model provides the SOC reference online. The proposed method is evaluated in simulation over multiple real-time driving cycles and compared with the DP results and two other real-time approaches. The effect of prediction horizon length is also studied. The simulation results demonstrate that the developed method can optimize battery life and fuel consumption. The results indicate 93%–97% matching to those of optimal controller, that is much better compared to the two other tested approaches.

Suggested Citation

  • Hassanzadeh, Mojtaba & Rahmani, Zahra, 2022. "A predictive controller for real-time energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222005667
    DOI: 10.1016/j.energy.2022.123663
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    References listed on IDEAS

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    1. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
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    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. Huijun Yue & Jinyu Lin & Peng Dong & Zhinan Chen & Xiangyang Xu, 2023. "Configurations and Control Strategies of Hybrid Powertrain Systems," Energies, MDPI, vol. 16(2), pages 1-18, January.
    3. Abd-Elhaleem, Sameh & Shoeib, Walaa & Sobaih, Abdel Azim, 2023. "A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm," Energy, Elsevier, vol. 265(C).

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