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An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles

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  • Yang, Chao
  • Liu, Kaijia
  • Jiao, Xiaohong
  • Wang, Weida
  • Chen, Ruihu
  • You, Sixiong

Abstract

To enhance the energy management strategy (EMS) effect on improving the fuel economy of plug-in hybrid electric vehicle (PHEV), the method of optimizing the key parameters of EMS has become a common solution. However, there is still a certain gap between current fuel consumption and its theoretical optimum level of existing EMSs. The reasons might be that more control parameters of the EMS need to be optimized and the performance of the optimization algorithm should also be improved. Regard at this, this paper proposes an intelligent EMS for PHEVs using a novel adaptive firework algorithm (AFWA) for the efficient optimization of control parameters. The EMS includes a rule-based gear shift strategy, maintaining the driving shaft always rotating within a reasonable range by considering vehicle velocity, acceleration and current gear position, and Takagi-Sugeno fuzzy control-based torque distribution strategy, optimizing the engine operating points according to demand torque of powertrain and battery state of charge. Meanwhile, a modified AFWA is firstly proposed to efficiently optimize control parameters of these two strategies by more reasonably tune the search area of the core firework according to the number of iterations. Finally, the proposed EMS is verified and evaluated through simulation and HIL test platform.

Suggested Citation

  • Yang, Chao & Liu, Kaijia & Jiao, Xiaohong & Wang, Weida & Chen, Ruihu & You, Sixiong, 2022. "An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023689
    DOI: 10.1016/j.energy.2021.122120
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Weida & Chen, Yincong & Yang, Chao & Li, Ying & Xu, Bin & Xiang, Changle, 2022. "An enhanced hypotrochoid spiral optimization algorithm based intertwined optimal sizing and control strategy of a hybrid electric air-ground vehicle," Energy, Elsevier, vol. 257(C).
    3. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    4. 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.
    5. Yin, WanJun & Qin, Xuan, 2022. "Cooperative optimization strategy for large-scale electric vehicle charging and discharging," Energy, Elsevier, vol. 258(C).
    6. 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).
    7. 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).
    8. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    9. 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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